Identification of bandwidth-heavy Internet traffic is important for network administrators to throttle high-bandwidth application traffic. Flow features based classification have been previously proposed as promising method to identify Internet traffic based on packet statistical features. The selection of statistical features plays an important role for accurate and timely classification. In this work, we investigate the impact of packet inter-arrival time feature for online P2P classification in terms of accuracy, Kappa statistic and time. Simulations were conducted using available traces from University of Brescia, University of Aalborg and University of Cambridge. Experimental results show that the inclusion of inter-arrival time (IAT) as an online feature increases simulation time and decreases classification accuracy and Kappa statistic.
Campus realities: forecasting user bandwidth utilization using Monte Carlo si...IJECEIAES
This document describes a study that used Monte Carlo simulation to forecast user bandwidth utilization in a campus network. The study collected traffic data from a test campus network setup over 30 days. It analyzed the data to determine bandwidth usage patterns and categorized usage. It then used the data to develop a Monte Carlo simulation model that generated random numbers based on the actual usage data probability distribution. This allowed the model to forecast bandwidth utilization for different days, helping network planners understand demands and design network upgrades accordingly. The model and results help campus networks better plan for high and normal traffic loads to deliver content.
Tracing of voip traffic in the rapid flow internet backboneeSAT Journals
Abstract
VoIP traffic application gaining a terrific admiration in the recent couple of years. VoIP Traffic Classification has concerned for network management and it comes to be more complicating because of modern applications behaviors and it has attracted the research community to develop and propose various classification techniques which don’t depend on ‘well known UDP or TCP port numbers. To overcome the problem of unknown flow classification and achieve effective network classification, a new innovative novel work called Multi Stage Fine-Grained classifier is proposed in this research for classifying the VoIP traffic flow with high accurate classification. The datasets of VoIP network traffic measurements taken from our campus WI-FI and the experimental results shows that the proposed work outstrips the existing approaches in the Rapid flow Internet Backbone. Without investigate the packet payloads, our proposed Fine-Grained classifier effectively classifies the Peer-to-Peer encrypted traffic in the real time network. Our experimental results shows high accuracy and small error rate in classifying the Peer-to-Peer network traffic.
Keywords: Multi Stage Fine-Grained Classifier, Rapid VoIP traffic Flow (SKYPE, VoIP, GAMING, Other) classification, Machine Learning
Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy LogicTELKOMNIKA JOURNAL
In mobile ad hoc networks, route selection is one of the most important issues that is studied in
these networks as a field of research. Many articles trying to provide solutions to choose the best path in
which the important parameters such as power consumption, bandwidth and mobility are used. In this
article, in order to improve the solutions presented in recent papers parameters such as power remaining,
mobility, degree node and available bandwidth are used by taking the factors for each parameter in
proportion to its influence in choosing the best path. Finally, we compare the proposed solution with the
three protocols IAOMDV-F, AODVFART and FLM-AODV with the help of OPNET simulation program
based on network throughput, routing discovery time, the average number of hops per route, network
delay.
This document evaluates shallow and deep network models for analyzing Secure Shell (SSH) traffic. It describes extracting flow feature statistics from network traffic and inputting them into recurrent neural networks (RNNs) and long short-term memory (LSTM) models for classification. The models are tested on public and private network trace datasets for their ability to classify SSH traffic and background applications over SSH versus non-SSH traffic. Deep learning models performed better than machine learning algorithms at traffic classification across different training and testing dataset configurations.
A novel token based approach towards packet loss controleSAT Journals
This document summarizes a research paper that proposes a novel congestion control mechanism called Stable Token-Limited Congestion Control (STLCC). STLCC monitors inter-domain traffic rates and limits the number of tokens to control congestion and improve network performance. The authors implemented STLCC in a prototype application and found that it was effective at controlling packet loss and improving network performance compared to other congestion control methods. They concluded that STLCC can automatically measure and reduce congestion to allocate network resources stably.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Probability Density Functions of the Packet Length for Computer Networks With...IJCNCJournal
The research on Internet traffic classification and identification, with application on prevention of attacks
and intrusions, increased considerably in the past years. Strategies based on statistical characteristics of
the Internet traffic, that use parameters such as packet length (size) and inter-arrival time and their
probability density functions, are popular. This paper presents a new statistical modeling for packet length,
which shows that it can be modeled using a probability density function that involves a normal or a beta
distribution, according to the traffic generated by the users. The proposed functions has parameters that
depend on the type of traffic and can be used as part of an Internet traffic classification and identification
strategy. The models can be used to compare, simulate and estimate the computer network traffic, as well
as to generate synthetic traffic and estimate the packets processing capacity of Internet routers
FORECASTING THE WIMAX TRAFFIC VIA MODIFIED ARTIFICIAL NEURAL NETWORK MODELSijaia
This paper attempts to present a new approach of forecasting the WiMAX traffic by exploiting Artificial
Neural Networks (ANN). To develop the model, actual data is gathered from the LibyaMax network that spans the duration of 180 days in total. Traffic data is separated into three cases based on the base stations involved (A, B and AB). The model implements traffic prediction by emphasizing on the maximum and minimum number of online user whereby two different learning algorithms are tested upon. to find the optimal one. Overall, the experimentation shows promising results of which the most severe error of prediction is not more than 0.0014. This indicates the feasibility of making accurate forecasting of both daily and weekly traffic of the WiMAX network based solely on the maximum and minimum number of users online.
This document provides summaries of 15 networking projects from TTA including the project code, title, description, and reference. The projects cover topics like delay analysis of opportunistic spectrum access MAC protocols, load balancing for network traffic measurement, key exchange protocols for parallel network file systems, anomaly detection in intrusion detection systems, and energy efficient group key agreement for wireless networks. The document provides contact information at the end for obtaining full project papers.
A COOPERATIVE LOCALIZATION METHOD BASED ON V2I COMMUNICATION AND DISTANCE INF...IJCNCJournal
Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment.
Vehicle positions obtained using V2I communication are more accurate because the known roadside unit
(RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends
more on the number of RSUs; however, the high installation cost limits the use of this approach. It also
depends on nonlinear localization nature. They were neglected in several research papers. In these studies,
the accumulated errors increased with time due to the linearity localization problem. In the present study,
a cooperative localization method based on V2I communication and distance information in vehicular
networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that
the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling
fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem,
but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed
method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the
proposed method has superior accuracy than existing methods, giving a root mean square error of
approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in
fault environments.
This document summarizes a research paper that proposes a semi-supervised machine learning approach for network traffic classification using the DBSCAN clustering algorithm. The approach uses labeled and unlabeled network flow data to first cluster the data and then assign class labels to the clusters based on the labeled data. It aims to overcome limitations of traditional port-based and payload-based traffic classification techniques by using only flow statistics for classification. The researchers tested their approach on the NSL-KDD dataset and found that DBSCAN achieved better effectiveness and efficiency than other methods.
A MARKOVIAN MODEL FOR INTERNET OF THINGS APPLICATIONIJCNCJournal
Internet of Things (IoT) allows communication among human-to-things, things-to-human, and things-tothings that are incorporated into an information networks allowing automatic information interchange and
the processing of data at real time. In this paper, we conduct a performance analysis of a real application
defined through four traffic classes with the priorities present in smart cities using Continuous Time
Markov Chains(CTMC). Based on a finite capacity queuing system, we propose a new cost-effective
analytical model with a push-out management scheme in favor of the highest priority (emergency) traffic.
Based on the analytical model, several performance measures for different traffic classes have been
studiedextensively including blocking probability; push out probability, delay, channel utilization as well as
overall system performance.
This document discusses next generation internet over satellite networks. It covers new services and applications for internet integrated services, modeling elastic and inelastic traffic, quality of service provisioning, traffic modeling techniques, and statistical methods for modeling different types of traffic including renewal processes and Markov models. It also discusses traffic engineering principles, multi-protocol label switching, internet protocol version 6, and the future development of satellite networking.
A Network and Position Proposal Scheme using a Link-16 based C3I SystemUniversity of Piraeus
The smart usage of hi-end military technological solutions in daily activities makes people life better. This paper describes a network and position proposal scheme in respect of technical networking and positioning information. A Link-16 based Command, Control, Communication and Intelligence (C3I) system is established among the mobile devices. Each device knows its geographical position using its GPS. A network along with a possible good position for user’s service is proposed, fulfilling his/her requirements for comfortable work.
Analytical Modelling of Localized P2P Streaming Systems under NAT ConsiderationIJCNCJournal
This document summarizes an analytical model for localized peer-to-peer (P2P) streaming systems that considers the impact of network address translation (NAT). It introduces theoretical boundaries for the number of peers that may be expelled from the system due to NAT incompatibility. It also presents a mathematical model for startup delay in P2P streaming that accounts for peers' NAT types. The document proposes a new neighbor selection algorithm that considers both autonomous system numbers and NAT types to improve connectivity while reducing transit traffic and startup delays.
Analysis of service-oriented traffic classification with imperfect traffic cl...IOSR Journals
This document proposes a new approach to network traffic classification called service-oriented traffic classification (SOTC). SOTC relies on identifying network services running on specific IP addresses and ports, and then classifying any traffic directed to that IP/port as belonging to that service. This reduces computational requirements compared to other methods. The accuracy of SOTC depends on correctly identifying the services in the initial stage. Evaluating SOTC on real traffic data confirmed it can improve classification accuracy while meeting scalability needs for large networks.
Performance Analysis of Different Modulation Schemes using Wi-Max And LTEIRJET Journal
This document analyzes the performance of different modulation schemes using WiMAX and LTE. It compares these two advanced wireless technologies in the physical layer and provides performance analysis of modulation schemes like BPSK, QPSK, and 16-QAM based on SNR or Eb/No and BER. MATLAB is used to simulate and analyze the performance of modulation schemes in WiMAX and LTE networks. The document also discusses the evolution of wireless access technologies and highlights the need for higher data rate technologies like WiMAX and LTE.
AOTO: Adaptive overlay topology optimization in unstructured P2P systemsZhenyun Zhuang
IEEE GLOBECOM 2003
Peer-to-Peer (P2P) systems are self-organized and
decentralized. However, the mechanism of a peer randomly
joining and leaving a P2P network causes topology mismatch-
ing between the P2P logical overlay network and the physical
underlying network. The topology mismatching problem brings
great stress on the Internet infrastructure and seriously limits
the performance gain from various search or routing tech-
niques. We propose the Adaptive Overlay Topology Optimiza-
tion (AOTO) technique, an algorithm of building an overlay
multicast tree among each source node and its direct logical
neighbors so as to alleviate the mismatching problem by choos-
ing closer nodes as logical neighbors, while providing a larger
query coverage range. AOTO is scalable and completely dis-
tributed in the sense that it does not require global knowledge
of the whole overlay network when each node is optimizing the
organization of its logical neighbors. The simulation shows that
AOTO can effectively solve the mismatching problem and re-
duce more than 55% of the traffic generated by the P2P system itself.
This document summarizes an article that proposes a novel approach called NOFITC (Near Real Time Online Flow-based Internet Traffic Classification) for online network traffic classification using machine learning. The approach customizes an open source C4.5 algorithm to work for online classification of NetFlow data in real-time. It evaluates the accuracy and processing time of the approach by comparing its performance to Weka's C4.5 implementation and a packet sniffing program on collected network traffic data. The results show that the accuracy is identical to C4.5 and it can classify NetFlow packets with no packet loss due to parallel processing, demonstrating it can perform online traffic classification in real-time.
THE DEVELOPMENT AND STUDY OF THE METHODS AND ALGORITHMS FOR THE CLASSIFICATIO...IJCNCJournal
This document summarizes a study on developing methods and algorithms for classifying data flows of cloud applications in the network of a virtual data center. The researchers developed a hybrid approach using data mining and machine learning methods to classify traffic flows in real-time. They created an algorithm for classifying and adaptively routing cloud application traffic flows, which was implemented as a module in the software-defined network controller. This solution aims to improve the efficiency of handling user requests to cloud applications and reduce response times.
Classification of Software Defined Network Traffic to provide Quality of ServiceIRJET Journal
This document discusses classifying network traffic using machine learning to provide quality of service in software defined networks. It aims to classify traffic by application to prioritize user required traffic and restrict unnecessary traffic like from over-the-top platforms to improve quality of service. The document reviews several related works applying techniques like naive bayes, support vector machines, and fuzzy logic for traffic classification and management in software defined networks to improve quality of service metrics.
Analysis of IT Monitoring Using Open Source Software Techniques: A ReviewIJERD Editor
The Network administrators usually rely on generic and built-in monitoring tools for network
security. Ideally, the network infrastructure is supposed to have carefully designed strategies to scale up
monitoring tools and techniques as the network grows, over time. Without this, there can be network
performance challenges, downtimes due to failures, and most importantly, penetration attacks. These can lead to
monetary losses as well as loss of reputation. Thus, there is a need for best practices to monitor network
infrastructure in an agile manner. Network security monitoring involves collecting network packet data,
segregating it among all the 7 OSI layers, and applying intelligent algorithms to get answers to security-related
questions. The purpose is to know in real-time what is happening on the network at a detailed level, and
strengthen security by hardening the processes, devices, appliances, software policies, etc. The Multi Router
Traffic Grapher, or just simply MRTG, is free software for monitoring and measuring the traffic load
on network links. It allows the user to see traffic load on a network over time in graphical form.
Automated Traffic Classification And Application Identification Using Machine...Jennifer Daniel
This document proposes a novel method for classifying network traffic flows using unsupervised machine learning. Statistical flow characteristics are used to automatically classify flows without relying on port numbers or protocol decoding. The method is evaluated using real network traffic traces, with the goal of optimizing classification accuracy while minimizing computational resources. Feature selection is used to determine an optimal set of statistical flow attributes for classification. The method provides benefits for network management, security, and traffic engineering.
Recently with the increasing development of distributed computer systems (DCSs) in networked
industrial and manufacturing applications on the World Wide Web (WWW) platform, including service-oriented
architecture and Web of Things QoS-aware systems, it has become important to predict the Web performance.
In this paper, we present Web performance prediction in time by making a forecast of a Web resource
downloading using the Efficient Turning Bands (TB) geostatistical simulation method. Real-life data for the
research were obtained from our own website named "Distributed forecasting system". Generation of log file
form website and performing monitoring of a group of Web clients from connected LAN. For better web
prediction we used spatio temporal prediction method with time utility for downloading particular file from
website and calculate forecasting result using Turning bands method but improving more forecasting
accuracy use the efficient turning band method basically efficient turning band use Naive bays algorithm and
calculate efficient result and that result is compared with Turning band and efficient turning band method.
The efficient turning band method result show good forecasting quality of Web performance prediction and
forecasting.
This document summarizes a research paper that proposes a semi-supervised machine learning approach using the DBSCAN algorithm to classify network traffic. The researchers use only flow statistics to cluster and classify network traffic into different application categories. They test their approach on the NSL-KDD dataset, which includes various types of attacks and normal traffic. Experimental results show that DBSCAN effectively classifies the network traffic with good accuracy and efficiency.
This document discusses strategies for bandwidth management and capacity planning for IP and 3G networks. It describes how traditional models like Poisson and Erlang are insufficient for today's internet traffic, which exhibits self-similar and multifractal properties. The document outlines different traffic modeling approaches, including self-similar and multifractal models, and how they can be applied to network planning, traffic engineering, and capacity forecasting. Case studies analyzing real network traffic traces are presented.
Efficient P2P data dissemination in integrated optical and wireless networks ...TELKOMNIKA JOURNAL
The Quality of Service (QoS) resource consumption is always the tricky problem and also
the on-going issue in the access network of mobile wireless part because of its dynamic nature of network
wireless transmissions. It is very critical for the infrastructure-less wireless mobile ad hoc network that is
distributed while interconnects in a peer-to-peer manner. Toward resolve the problem, Taguchi method
optimization of mobile ad hoc routing (AODVUU) is applied in integrated optical and wireless networks
called the adLMMHOWAN. Practically, this technique was carry out using OMNeT++ software by building
a simulation based optimization through design of experiment. Its QoS network performance is examined
based on packet delivery ratio (PDR) metric and packet loss probabilities (PLP) metric that consider
the scenario of variation number of nodes. During the performing stage with random mobile connectivity
based on improvement in optimized front-end wireless domain of AODVUU routing, the result is performing
better when compared with previous study called the oRia scheme with the improvement of 14.1% PDR
and 43.3% PLP in this convergence of heterogeneous optical wireless network.
Review on IoT Based Bus Scheduling System using Wireless Sensor NetworkIRJET Journal
The document reviews an IoT-based bus scheduling system using wireless sensor networks. It proposes a model that uses GPS, WiFi, and RFID technologies to track bus locations in real-time and provide this information to passengers through a mobile app. This will help reduce waiting times and congestion. The model also includes features like automated ticketing through smart RFID cards, passenger counting sensors, and an emergency input to alert authorities in case of incidents on the bus. The system is aimed at improving efficiency of bus transportation services.
Handover Algorithm based VLP using Mobility Prediction Database for Vehicular...IJECEIAES
This paper proposes an improved handover algorithm method for vehicle location prediction (VLP-HA) using mobility prediction database. The main advantage of this method is the mobility prediction database is based on real traffic data traces. Furthermore, the proposed method has the ability to reduce handover decision time and solve resource allocation problem. The algorithm is simple and can be computed very rapidly; thus, its implementation for a high-speed vehicle is possible. To evaluate the effectiveness of the proposed method, QualNet simulation is carried out under different velocity scenarios. Its performance is compared with conventional handover method. The superiority of the proposed method over conventional handover method in deciding the best handover location and choosing candidate access points is highlighted by simulation. It was found that VLP-HA has clearly reduced handover delay by 45% compared to handover without VLP, give high accuracy, hence low complexity algorithm.
Internet ttraffic monitering anomalous behiviour detectionGyan Prakash
This document discusses a methodology for monitoring internet traffic and detecting anomalous behavior. It begins by noting the challenges of understanding vast quantities of internet traffic data due to the diversity of applications and services. Recent cyber attacks have made it important to develop techniques to analyze communication patterns in traffic data for network security purposes.
The proposed methodology uses data mining and entropy-based techniques to build behavior profiles of internet backbone traffic. It involves clustering traffic based on communication patterns, automatically classifying behaviors, and modeling structures for analysis. The methodology is validated using data sets from internet core links. It aims to automatically discover significant behaviors, provide interpretations, and quickly identify anomalous events like scanning or denial of service attacks.
IRJET- Comparative Study on Embedded Feature Selection Techniques for Interne...IRJET Journal
This document presents a comparative study on embedded feature selection techniques for internet traffic classification. It discusses using machine learning and deep learning algorithms like Naive Bayes, Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) on network traffic datasets to classify traffic. The performance of these techniques is analyzed in terms of error metrics, with the results showing that the proposed deep learning approach using MLP outperforms existing machine learning techniques. The document also provides background on internet traffic classification and related work applying techniques like K-means clustering, C5.0 decision trees, and SVM to classify network application traffic based on flow features.
This document discusses the need for network simulation tools to test telecom network components before deployment. It describes the key requirements for building an efficient simulation tool that can accurately model a complex telecom network, including 3G and UMTS networks. Specifically, it discusses modeling internet traffic and using semi-Markovian models to generate traffic. It also covers the importance of considering physical layer factors like RF path loss and mechanisms like power control when simulating UMTS networks. The document provides details on the algorithms and architecture needed for a simulation tool to generate traffic according to specified models and evaluate network performance and capacity.
This document discusses the need for network simulation tools to test telecom network components before they are deployed. It describes the key requirements for building an efficient simulation tool that can accurately model a complex telecom network, including 3G and UMTS networks. Specifically, it discusses the need to generate realistic traffic patterns and loads, model protocols and interfaces, and consider physical layer factors like RF path loss and power control mechanisms. The document provides details on using semi-Markovian models to generate traffic according to different states and distributions. It also outlines the overall architecture of a packet load generator tool to simulate network elements and evaluate their performance under different traffic scenarios.
Choosing the best quality of service algorithm using OPNET simulationIJECEIAES
The concept of quality of service (QoS) is a new computer technology. Previously, there was a slow internet connection to access the sites and it was slow to send information. But now, it requires speeding up the traffic and increasing the efficiency for audio and video. In this study, we discuss the concepts of QoS provided over the network to achieve these goals. This study aims to compare six algorithms to control the QoS, then, the best algorithm will be selected to improve the traffic. These algorithms are named first in first out (FIFO), priority queuing (PQ), custom queuing (CQ), CQ with low latency queuing (LLQ), weighted fair queuing (WFQ), WFQ with low latency queuing (LLQ), so the behavior of these algorithms can be measured. The results obtained by comparing between them using OPNET simulation show that the best algorithm is the priority queuing algorithm, followed by CQ, then CQ with LLQ, then WFQ, then WFQ with LLQ and finally FIFO. All these results are plotted in the form of graphs to show the paths of these algorithms for the single state with an operation time of 5 minutes for each algorithm.
This document provides a comprehensive literature review and analysis of various traffic prediction techniques. It begins with an abstract that outlines the need for accurate traffic forecasting to address issues caused by increased road traffic. The document then reviews several existing traffic prediction methods and technologies, including fuzzy logic-based systems, intelligent traffic signal controllers, dynamic traffic information systems, and frameworks that utilize IoT, cloud computing, and machine learning. It identifies gaps in current literature, such as a lack of sensor data and advanced application frameworks for prediction. Finally, the document presents several comparison tables analyzing traffic prediction techniques based on the datasets, parameters, merits and demerits of each approach. The overall purpose is to conduct a systematic analysis of past work and identify future research
IRJET - A Review on Congestion Control Methods in Mobile Adhoc NetworksIRJET Journal
This document reviews different techniques for congestion control in mobile ad hoc networks (MANETs). It begins with an introduction to MANETs and discusses how congestion can occur and degrade network performance. The literature survey section then summarizes several recent studies that have proposed various congestion control methods. These include adaptive data rate and control of hello packets, cross-layer approaches, hop-by-hop congestion control algorithms, avoiding congestion by monitoring bandwidth capacity, and fuzzy logic based congestion control. The document concludes that congestion is a major issue in MANETs and different control mechanisms aim to improve throughput, delivery ratio and reduce packet loss and delay.
This document summarizes previous literature on traffic analysis and congestion modeling in mobile networks. It reviews works that have evaluated network performance at different elements like the BTS, BSC and MSC. However, none addressed congestion at all three basic elements (BTS, BSC, MSC) to characterize end-to-end connections, or used busy hour traffic data to adequately dimension network elements. The document also identifies gaps in the existing research, such as not establishing the statistical causes of congestion or using sufficient data. It proposes to analyze traffic at the access and core networks using live network data over two years to help dimension elements and identify congestion causes to develop an accurate congestion prediction model.
Litrature Survey of Traffic Analysis and Congestion Modeling In Mobile Network iosrjce
Network congestion is one of the major problems of GSM service providers as the number of
subscribers increase and new services are introduced. All the proposed techniques in literatures for controlling
congestion are centered on two principles which are either to reject excessive traffic to prevent over-utilization
of network resources or diverting excess load if overload occurs. These techniques do not specify how network
resource can be provided to absorb rejected or diverted traffic so that revenue will not be lost during congestion
and hence, they do not really address congestion during busy hour. Real-time traffic analysis is required to
understand user traffic demand pattern on network resources for proper prediction of network congestion so
that resources can be provided to take care of rejected or diverted traffic. However, available literature survey
on mobile network congestion modeling showed that none of the existing literature: address congestion at the
three basic elements of GSM network to characterize end-to-end connection; use busy hour traffic data to
adequately dimension GSM network elements so that the network can cope with load B. Therefore, effective
congestion control mechanism that can take these research gaps into consideration for proper forecasting and
efficient dimension of the network resources to address busy hour congestion must be developed. This paper is a
preliminary report on development of such accurate congestion prediction model through an ongoing research
work using real live network data from one of the Service provider’s networks in Abuja, Nigeria as a case study
Similar to Impact of Packet Inter-arrival Time Features for Online Peer-to-Peer (P2P) Classification (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Unblocking The Main Thread - Solving ANRs and Frozen FramesSinan KOZAK
In the realm of Android development, the main thread is our stage, but too often, it becomes a battleground where performance issues arise, leading to ANRS, frozen frames, and sluggish Uls. As we strive for excellence in user experience, understanding and optimizing the main thread becomes essential to prevent these common perforrmance bottlenecks. We have strategies and best practices for keeping the main thread uncluttered. We'll examine the root causes of performance issues and techniques for monitoring and improving main thread health as wel as app performance. In this talk, participants will walk away with practical knowledge on enhancing app performance by mastering the main thread. We'll share proven approaches to eliminate real-life ANRS and frozen frames to build apps that deliver butter smooth experience.
Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.
In May 2024, globally renowned natural diamond crafting company Shree Ramkrishna Exports Pvt. Ltd. (SRK) became the first company in the world to achieve GNFZ’s final net zero certification for existing buildings, for its two two flagship crafting facilities SRK House and SRK Empire. Initially targeting 2030 to reach net zero, SRK joined forces with the Global Network for Zero (GNFZ) to accelerate its target to 2024 — a trailblazing achievement toward emissions elimination.
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
Enhancing Security with Multi-Factor Authentication in Privileged Access Mana...Bert Blevins
In the constantly evolving field of cybersecurity, ensuring robust protection for sensitive data and critical systems has never been more vital. As cyber threats grow more sophisticated, organizations continually seek innovative ways to bolster their defenses. One of the most effective tools in the security arsenal is Multi-Factor Authentication (MFA), particularly when integrated with Privileged Access Management (PAM).
Privileged Access Management encompasses the methods, procedures, and tools used to regulate and monitor access to privileged accounts within an organization. Users with privileged accounts possess elevated rights, enabling them to perform essential operations such as system configuration, access to sensitive data, and management of network infrastructure. However, these elevated privileges also pose a significant security risk if they fall into the wrong hands.
By combining MFA with PAM, organizations can significantly enhance their security posture. MFA adds an additional layer of verification, ensuring that even if privileged account credentials are compromised, unauthorized access can be thwarted. This integration of MFA and PAM provides a robust defense mechanism, protecting critical systems and sensitive data from increasingly sophisticated cyber threats.
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feature selection is a vital task to improve the classification and identification performance compared to
selection of the classification algorithm. Presently, several feature selection algorithms have been introduced,
e.g., [7]-[11]. However, most of the introduced methods do not consider the impact of integrating online
features with inter-arrival time (IAT) for online P2P classification.
This paper proposes an approach based on analytic methods one-way analysis of variance and
incremental traffic classification algorithm. One-way analysis of variance is implemented using KNAME
tool and Hoeffding Tree incremental machine learning algorithm is implemented using MOA (Massive On-
line Analysis) tool in order to investigate the impact of packet IAT feature for online P2P classification.
The remainder of this paper is organized as follows. Section 2 introduces related works including
ML concepts, traffic classification and feature selection. Section 3 discusses the methodology to investigate
the impact of packet inter-arrival time feature for online P2P classification. The experimental setup, result
and discussion are discussed in Section 4. Section 5 presents the conclusion.
2. RELATED WORK
Machine learning (ML) is apromising technique that has been used for data mining and knowledge
discovery [12]. Unsupervised learning strategies basiclly clusters flows with similar parttern behaviour.
Supervised learning needs a set of labeled data to train its model in advance for identification and
classification of data [12].
Classification using flow features mainly deploys machine learning to perform training and
classification. From the extracted flow features, the classifier predicts the class of new flow. This process is
called a data mining problem. The first work using this technique was by [13]. Generally, classification can
be performed in three steps, extracting the features, selection of feature and generating classifier [14].
Moore et al. [15] has suggested 249 features that can be potentially used in ML traffic identification.
However most of these features can only be obtained in an off-line mode. Off-line features such as maximum
and minimum bytes in packet only can be obtained with complete flows. Work in [16] employed all 249
features suggested in [15] derived from packet streams consisting of one or more packet headers. Most of
these features cannot be extracted online from live traffic for online traffic identification.
Feature selection (FS) is used to select optimal subset features from the input which can efficiently
describe the input data while reducing effects from irrelevant or noise features yet still provide good
prediction of its class [7], [17]. Traffic identification can be improved with reference to computational
performance and accuracy by using the most relevant features [18].
Loo et al. [8] proposed 12 online features without features related time. Monemi et al. [19] has
proposed 35 real-time flow features that can be easily extracted from flow records. These flows include
number of packets, port address, protocol, overall Transmission Control Protocol (TCP) flags, average
volume in byte, volume in byte per packet, flow duration, payload volume in byte, flow duration, average
number of packet per second, average volume in byte per second, average payload volume in byte per
second, average payload volume in byte per packet, and average time interval. Erman et al. [16] has
performed backward greedy search on various datasets and found that the use of time-related features such as
duration, IAT and flow throughput are not useful in traffic classification.
Online features techniques have been proposed in [7], [20]. These works used Cambridge datasets
and Naive Bayes to evaluate two feature selection algorithms named Bias Coefficient Results (BFS) and
Selected Online Feature. These works achieved accuracy of 90.92% and 93.20%, respectively. Besides, the
work in [7] has considered IAT as one of the proposed on-line features.
Most researches have focused on online features with IAT as suggested in [7], [11], [19], [21].
However, the impact of packet inter-arrival time feature for online P2P classification still plays an important
role for accurate and timely classification.
3. OVERVIEW OF THE METHODS
Our proposed method to invisticate the impact of packet IAT feature for online P2P classification
consist of two main stages, test the signficance of packet IAT feature and investigate the impact of packet
IAT feature for online P2P identification with reference to accuracy, kappa statistic and time. The first stage
one-way analysis of variance analytics using KNAME tool to test the signficance of IAT. In the second stage,
Hoeffding Tree incremental machine learning algorithm is implemented in MOA tool. All stages will be
discussed in details in Section 3.1. Figure 1 shows the overview of the proposed method to investigate the
impact of packet IAT feature for online P2P classification.
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Figure 1. Proposed method
3.1. Techniques for analyzing features
Konstanz Information Miner (Knime) is a recent open-source data analytics platform that allows for
undertaking complete statistics and data mining analysis. One-way ANOVA is implemented in KNIME
benchmark [22]. WEKA workspace tools also is used for classification [23]. One-way ANOVA is the most
effective method available for analyzing the more complex data sets [24]. In this work, we computed the F-
statistic using ANOVA. Equations (5) and (1) represents sum of square (SS) in ANOVA. While the sum of
squares for Treatment (SST) is given by Equation (2). Sum of squares for Error (SSE) is computed using
Equation (3). The Variance between Treatments (MST) is computed by Equation (4). The VarianceWithin
Treatments (MSE) is computed using Equation (5). F-statistic is obtained by dividing MST to MSE is given
by Equation (6). Using 95% confidence interval for mean difference, ANOVA is calculated as:
∑ ∑ ( ) (1)
∑ ∑ ( )
∑ ∑ ( ) ∑ ∑ ( )
∑ ∑ ( ) (2)
∑ ∑ ( ) (3)
Then
(4)
(5)
(6)
Thus, with ANOVA test null hypothesis , which means that there are no treatment
effects. Where bar is the samples mean, is the sample size, is the specified population mean.
Massive Online Analysis (MOA) [25]: MOA is a data stream mining suite that was written in Java.
Userscan use MOA using Graphic User Interface (GUI) or through command lines. Different from WEKA
[23] whichis for batch data mining, MOA specializes on processing and analyzing data streams. The suite
includes evaluation tools such as concept drift evaluation, and interleave-test-then-train evaluation. It is also
built with a collection of data stream identification techniques such as Naive Bayes, Hoeffding Tree, Bagging
and Boosting techniques. In this paper, MOA is used to analyze the impact of integrating online features with
IAT for online P2P classification.
4. EXPERIMENTAL SETUP, RESULTS AND DISCUSSION
This section, presents and dicusses the network traffic datasets used and the evaluation method used
to evaluate the impact of integrating online features with inter-arrival time for online P2P classification.
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4.1. Dataset
Network traces are used to validate the performance of the proposed technique. These datasets are
PAM [26], UNIBS [27] and Cambridge [15]. Table 1 summarizes the used datasets, which the description of
each dataset as follows:
a. PAM traces was captured in Aalborg University from 25th February 2013 to 1st May 2013 and reported
in [26]. The label of the dataset was collected using Volunteer-Based System (VBS). A total of 1,262,022
flows were captured, where 535,438 flows were labeled as reported in [26]. However, only 339,061 flows
could be used as most flows have less than five packets and the netflow and feature extractor modules
only extract flows that contain five packets or more. By using the provided information files, the flows
are labeled into four classes: WEB, FTP, P2P, and Others.
b. The UNIBS datasets [27] were obtained from a series of workstations at the University of Bresciafrom
30th
September 2016 to 2nd October 2016. The traces are collected on edge router, where the traffic was
generated by 20 workstations running GT toolset. In this work, the traces were processed using netflow
and feature extractor based on 1 minutes timeout and flows with a minimum of five packets are extracted.
A total of 77,303 flows are extracted and all flows features are extracted based on only the first five
packets of each flow. The accompanied groundtruth labels, were use to classify flows into five classes,
P2P, Skype,Web, Others, and Mail.
c. The Cambridge datasets were obtained from traces captured on the Genome Campus network in August
2003 in the University of Cambridge [15]. There are ten different datasets each from a different period of
the 24-hour day. These datasets consist of TCP flow. Furthermore, every flow sample is high dimensional
since it contains 248 features. The dataset applications with negligible classes such as games and
interactive were excluded as it is insufficient for training and testing. These include classes such as FTP-
Pasv, Attack, P2P, Database, Multimedia, Web, Mail, FTP-Control, and Services.
Tabel 1. Datasets Statistics
UNIBS PAM Cambridge
#Flow instances 77,303 339,061 397,030
#Classes 5 4 10
#Flow features extracted 9 9 9
4.1.1. Dataset preprocessing
Online features are extracted and online features with IAT and without IAT as suggested in our
previous work [28] are selected. For the UNIBS and PAM datasets, the features are extracted based on the
first five packets statistic of each flow. However, for the Cambridge dataset, the statistics of the first 5
packets are not available without access to the raw packets. Thus, for this dataset, the complete flow statistic
is used (not only first 5 packets). In order to have a fair comparison of all datasets, the mean features in
Cambridge dataset are modified to total features. Table 2 shows the list of feature that had been extracted.
Table 2. Online features with IAT
# Name Description
1 Port_a Source port number
2 Port_b Port b Destination port number
3 Ply_size_ba Total byte in IP packet(Downlink)
4 Ply_size Total byte in IP packet
5 Pck_size_ba Total byte in Ethernet packet(Downlink)
6 Pck_size Total byte in Ethernet packet
7 iat_ba Total packet inter-arrival time(downlink)
8 Iat Total packet inter-arrival time
9 Class Protocol
4.1.2. Evalution
Prequential evaluation using fading factors forgetting mechanism proposed by Gama et al. [29] is
adopted as the evaluation method. This method is suitable for evaluating incremental learning algorithm. The
prequential parameters used in our experiment are as stated below, unless specified otherwise:
a. Classifier to train: Hoeffding Tree
b. Stream to learn from: PAM, Cambridge and UNIBS dataset
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c. Training and testing on a total of 250,000 samples for PAM and Cambridge, while UNIBS 50000
samples.
d. Testing every two hundred samples
e. Instances between memory bound checks: 193,000 samples for PAM and Cambridge, while UNIBS
40000 samples
f. Evaluate Prequential Parameters: Window Classification Performance Evaluator
g. Size of sliding is 1,000
The performance indicators used in this research are classification time , Kapaa statistic K = 1
and average accuracy (Acc). Average accuracy is the overall accuracy for a dataset. Let the total correct
identification in a dataset with (N) flow instances is η. The performance indicators used in this paper are:
(7)
(8)
while : classifier’s prequential accuracy is: probability of correct prediction. Kappa has preferable
properties such that value of 1 with perfect agreement ( ) is used. The value approximately zero when
the observed agreement is almost the same as would be expected by chance ( ). Furthermore, Kappa
statistic does not assume marginal probabilities to be the same for different observers.
4.2. One-way ANOVA test results
This subsection explains the significant of selected features by using ANOVA test with 95%
confidence interval for the mean difference. The result explains all selected features are significant because
after tested with ANOVA the P-value less than 0.05. Also, this test explains the IAT features are less
significant than other features as shown in Figure 2.
4.3. Online classification results
The experimental results presented in Figure 3 to Figure 8, illustrate the effect of IAT inclusion as
an online feature for P2P identification. The result as presented in Table 3 indicates that packet IAT feature
as online feature decreases identification accuracy and Kapaa statistic. Furthermore, packet IAT feature
increases the experimental evaluation time. This is as a result of packet IAT feature morphing which involves
alternation on direction pattern which is dependent on network locations. Also these results prove previous
offline studies that:
a. Time-related features do not help to distinguish among applications [20], [30].
b. The use and statistical features of application dependent only on inter-packet time is a challenging task
due to the time required by an application to generate and transfer packets to the transport layer is masked
by the fact that additional time is added due to the network conditions and the TCP layer [31].
Table 3. Classification Results
Cambridge Online features without IAT Online features with IAT
Accuracy mean 98.86 98.80
Kappa statistic mean 59.46 59.46
CPU total time per second mean 1.41 2.28
UNIBS dataset Online features without IAT Online features with IAT
Accuracy mean 94.13 93.15
Kappa statistic mean 87.87 86.16
CPU total time per second mean 0.51 0.74
PAM dataset Online features without IAT Online features with IAT
Accuracy mean 92.42 92.22
Kappa statistic mean 17.71 15.42
CPU total time per second mean 1.56 1.82
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Figure 2. Screen shot of test statistic ANOVA
Figure 3. UNIBS dataset mean classification accurcay
Figure 4. UNIBS dataset mean kapaa statistic
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Figure 5. UNIBS dataset evalution time
Figure 6. PAM dataset mean classification accurcay
Figure 7. PAM dataset mean kapaa statistic
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Figure 8. PAM dataset evalution time
5. CONCLUSION
In this paper, we investigated the impact of packet IAT feature for online P2P classification with
reference to accuracy, kappa statistic and evaluation time. The simulation results indicate that the packet IAT
features for online P2P classification decrease accuracy and Kappa statistic, and also increase evaluation
time. These results because IAT morphing usually involves alternation on direction pattern and depend on
different network locations. The acknowledgment section is optional. The funding source of the research can
be put here.
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BIOGRAPHIES OF AUTHORS
Bushra Mohammed Ali, is a PhD candidate at the Faculty of Electrical Engineering Universiti
Teknologi Malaysia. He obtained B.Sc. and M.Sc.in Computer Engineering and Networks-Faculty of
Engineering and Technology, University of Gezira, Sudan. He is a lecturer at the faculty of Computer
and Statistics Studies, University of Kordofan. His research interests include computer architecture,
Network Traffic classification and control, Artificial Intelligence and optimization techniques.
Mosab Hamdan is a PhD student at Vecad research group in University Technology Malaysia
(UTM). He obtained Bachelor degree in Electronic and Electrical engineering from University of
Science and Technology (Sudan) in 2010, and Msc in computer architecture and networking at
University of Khartoum (Sudan) in 2014. His current research interests are Software Defined
Networking, Load Balancing, Traffic Classification, and Future Network.
Mohammed Sultan Mohammed received his BSc in Computer Engineering from Hodeidah
University (Yemen) in 2005. He received the MSc in Computer Engineering and Networks from the
University of Jordan (Jordan) in 2015. He is currently pursuing his Ph.D. study at Univesiti Teknologi
Malaysia. His research interests are parallel processing, multi-core embedded systems, System-on-
Chip (SoC).
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Joseph Stephen Bassi received his Ph.D. degree in Electrical Engineering from the Universiti
Teknologi Malaysia, in 2017, M.Eng. degree in Electrical & Electronics Engineering (Electronics)
from University of Maiduguri, Nigeria in 2012 and B.Tech degree in Computer Science &
Mathematics from Federal University of Technology Minna, Nigeria in 2000. He is currently a
Lecturer with the Department of Computer Engineering, Faculty of Engineering, University of
Maiduguri, Nigeria. His research interests are in Network algorithmic, Artificial Intelligence &
optimization techniques and computer communication networks.
Ismahani Ismail received her PhD degree in Electrical and Computer Engineering from Universiti
Teknologi Malaysia in 2013. She is a Senior Lecturer with the Faculty of Electrical Engineering,
Universiti Teknologi Malaysia. Her fields are in digital systems and network algorithmic.
Muhammad Nadzir Bin Marsono received the PhD in Electrical and Computer Engineering.
University of Victoria, Canada in 2007. He is now an Associate Professor at the Faculty of Electrical
Engineering, Univeristi Teknologi Malaysia. His research interests in system level integration,
working in multiple areas of embedded systems, specialized computer architectures, VLSI design,
network algorithmic, network-on-chip, and network processor architectures.