NDLI: Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine

Content Provider	IET Digital Library
Author	Han, Weijie Xue, Jingfeng Yan, Hui
Abstract	Network anomaly detection is an effective way for analysing and detecting malicious attacks. However, the typical anomaly detection techniques cannot perform the desired effect in the controlled network just as in the general network. In the circumstance of the controlled network, the detection performance will be lowered due to its special characteristics including the stronger regularity, higher dimensionality and subtler fluctuation of its traffic. On the motivation, the study proposes a novel classifier framework based on cross entropy and support vector machine (SVM). The technique first subtracts the representative traffic characteristics from the network traffic and defines a 7-tuple feature vector for the controlled network by extending the traditional 5-tuple representation of the usual network. Then the probability distributions and cross entropies of the 7 tuples are calculated during the defined statistical window so as to generate the 7-tuple cross-entropy feature vector for profiling the network traffic fluctuation in the controlled network. Finally, the multi-class SVM classifier is trained by importing the 7-tuple cross-entropy feature vectors. Experimental results show that the proposed classifier can achieve higher detection rates and is more suitable to be used in the controlled network than the typical detection techniques.
Starting Page	109
Ending Page	116
Page Count	8
ISSN	17518709
Volume Number	13
e-ISSN	17518717
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ifs/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ifs.2018.5186
Journal	IET Information Security
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-10-23
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	5-tuple Representation 7-tuple Cross-entropy Feature Vector Anomalous Traffic Detection Computer Communication Computer Network Security Controlled Network Data Security Entropy Knowledge Engineering Technique Malicious Attack Detection Network Anomaly Detection Network Traffic Fluctuation Pattern Classification Probability Probability Distribution Statistical Window Statistics Support Vector Machine SVM Classifier Telecommunication Traffic Traffic Characteristic
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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