AN EFFECTIVE APPROACH TOWARDS PARALLELIZATION OF NETWORK TRAFFIC ANOMALY DETECTION SYSTEM

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Published: Oct 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.4711
Keywords:
Network traffic, Network anomaly detection, speedup, efficiency, multi-core processor, parallelization

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Ashok Kumar D
D. Ashok Kumar Associate Professor, Department of Computer Science Government Arts College, Thuvakudimalai, Tiruchirappalli, India
SR Venugopalan
S. R. Venugopalan Scientist, Information and Computing technologies Aeronautical Development Agency (Ministry of Defence) Bangalore, India

Abstract

Network traffic data is huge in volume and needs to be processed in real time to detect Intrusions. By utilizing the power of latest Hardware with multi-core processors and GPGPU computing, there is a scope for processing the huge volume of network traffic data in near real-time. This study is intended for examining the potential of Network Anomaly Detection Algorithm (NADA) presented by the authors [9] for parallelization. NADA was parallelized using parallel toolbox functions in Matlab. Other classification algorithms such as Naive Bayes, SVM and Decision trees were also implemented using the pre-defined functions in Matlab and the time taken for execution of these algorithms were compared with NADA for various sizes of data. This study uses the new version of Kyoto University’s Intrusion Detection/Evaluation benchmark dataset for experimentation. The parallel performance measures such as time taken, speedup and efficiency are encouraging.

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Vol. 8 No. 8 (2017): September-October
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Author Biographies

Ashok Kumar D, D. Ashok Kumar Associate Professor, Department of Computer Science Government Arts College, Thuvakudimalai, Tiruchirappalli, India

Dr. D. Ashok Kumar is an Associate Professor in the Department of Computer Science, Government Arts College, Tiruchirappalli. His current research interests include Data Mining Algorithms, Pattern Matching and Information Security and Systems. His research works have appeared in a variety of international journals and international conference proceedings. He has guided several M. Phil. and Ph. D. Scholars.

SR Venugopalan, S. R. Venugopalan Scientist, Information and Computing technologies Aeronautical Development Agency (Ministry of Defence) Bangalore, India

S. R. Venugopalan holds M. Sc., M. Phil in Computer Science. He obtained his M.S (by research) in Management from IIT Madras and he is a Scientist in Information & Computing Technologies Directorate of Aeronautical Development Agency, Bangalore. His current research interests are in Information Technology and Information Security, Project Management, Product Lifecycle Management and Enterprise Information Systems and their implementation. His research works have appeared in of international journals and international conference proceedings.

References

Song, Jungsuk, Hiroki Takakura, and Yasuo Okabe. “Kyoto University Benchmark Data datasetâ€, November 2011. URL http://www.takakura.com/kyoto_data/.

The third international knowledge discovery and data mining tools competition dataset KDD99-Cup http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 1999.

Peddabachigari, Sandhya, Ajith Abraham, and Johnson Thomas. "Intrusion detection systems using decision trees and support vector Machines." International Journal of Applied Science and Computations, USA 11.3 (2004): 118-134.

Fernando Silva & Ricardo Rocha.Parallel and Distributed

Programming URL: http://www.dcc.fc.up.pt/~fds/aulas/PPD/1112/metrics_en.pdf. Accessed on 2 February 2016.

Foschini, Luca, et al. "A parallel architecture for stateful, high-speed intrusion Detection.â€International Conference on Information Systems Security. Springer, Berlin, Heidelberg, 2008.

Shanbhag, Shashank, and Tilman Wolf. "Accurate anomaly detection through parallelism."IEEE network 23.1 (2009): 22-28.

Ihsan, Zohair, Mohd Yazid Idris, and Abdul Hanan Abdullah. "Attribute normalization techniques and performance of intrusion classifiers: A comparative analysis."Life Science Journal. 10.4 (2013).

D. Ashok Kumar, and S. R. Venugopalan. "The Effect of Normalization on Intrusion Detection Classifiers (Naïve Bayes and J48)â€. International Journal on Future Revolution in Computer Science & Communication engineering, 3.7 (2017): 60-64.

D. Ashok Kumar, and S. R. Venugopalan. "A DISTANCE BASED ALGORITHM FOR NETWORK ANOMALY DETECTION USING INITIAL CLASSIFICATION OF'PROTOCOL TYPE'ATTRIBUTE." International Journal of Advanced Research in Computer Science 8.7 (2017).

Panda, Mrutyunjaya, and Manas Ranjan Patra. "Network intrusion detection using naive bayes."International journal of computer science and network security, 7.12 (2007): 258-263.

Hussein, Safwan Mawlood, Fakariah Hani Mohd Ali, and Zolidah Kasiran. "Evaluation effectiveness of hybrid IDs using snort with naive Bayes to detect attacks." Digital Information and Communication Technology and it's Applications (DICTAP), 2012 Second International Conference on. IEEE, 2012.

Amor, Nahla Ben, Salem Benferhat, and Zied Elouedi. "Naive bayes vs decision trees in intrusion detection systems."Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004.

Inadyuti Dutt, Samarjeet Borah. “Some Studies in Intrusion Detection using Data Mining Techniques.†International Journal of Innovative Research in Science, Engineering and Technology, 4.7 (2015):.5500-5511.