Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Salama, Abdussalam; Saatchi, Reza; Burke, Derek

doi:10.1109/mcsi.2017.43

Cited by 6 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The twelve features are features of Modbus_traffic data set selected by FAFS method. (4,6,9,11,12,18,25,16,22,23,14) 0.5737 (4,6,9,11,12,18,25,22,23,14,16) 0.5737 (4,6,9,11,12,18,25,16,23,14,22) 0.5737 (1,4,6,9,11,12,16,22,23,14,25) 0.5737 … … … … (1,4,6,9,11,12,22,23,14,25,…”

Section: Experimental Results and Analysismentioning

confidence: 99%

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Feng¹,

Kang²,

Zhang³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Feng¹,

Kang²,

Zhang³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Netem is a network emulation tools used to emulate packet loss, delay and jitter [18]. In this study this software was used to alter delay, jitter and percentage packet loss ratio between the communicating PC-1a and PC-1b.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Salama¹,

Saatchi²,

Burke³

2018

Preprint

View full text Add to dashboard Cite

Abstract:Organisations and individuals are increasingly relying on large computer networks to access information and to communicate multimedia type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of the information carrying packets, examining each packet's transmission parameters individually is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and thus they are important in multimedia networks. In this study an adaptive sampling technique based on regression and fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic's variations. Its performance was found to be superior to the conventional non-adaptive sampling methods.

show abstract

“…Netem is a network emulation tool used to emulate packet loss, delay, and jitter [26]. In this study this software was used to alter delay, jitter, and percentage packet loss ratio between the communicating PC-1a and PC-1b.…”

Section: Methodsmentioning

confidence: 99%

“…The Wireshark network monitoring captured RTP packets (installed on two of the PCs) that were sorted using their sequence numbers to determine end-to-end delay, jitter, and percentage packet loss ratio as outlined below [27][28][29].…”

Section: Network Traffic Parametersmentioning

confidence: 99%

Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

2018

View full text Add to dashboard Cite

Organisations such as hospitals and the public are increasingly relying on large computer networks to access information and to communicate multimedia-type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to the quantity of the data packets, examining each packet's transmission parameters is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and they are thus important in evaluating the performance of multimedia networks. In this study, an adaptive sampling technique based on regression and a fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic's variations. Its performance was found to be superior to the conventional nonadaptive sampling methods.

show abstract

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Cited by 6 publications

References 12 publications

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Contact Info

Product

Resources

About