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A Framework of Intrusion Detection System based on Bayesian Network in IoT

Volume 14, Number 10, October 2018, pp. 2280-2288
DOI: 10.23940/ijpe.18.10.p4.22802288

Qingping Shia, Jian Kanga, Rong Wangb, Hang Yic, Yun Linc, and Jie Wangc

aBeijing Insititute of Astronautical Systems Engineering, Beijing, 100076, China
bChina Academy of Launch Vehicle Technology, Beijing, 100076, China
cHarbin Engineering University, Harbin, 150001, China

(Submitted on June 21, 2018; Revised on July 13, 2018; Accepted on August 14, 2018)

Abstract:

The increasing popularity of Internet of Things (IoT) technology has greatly influenced the production mode and life quality of humans. Simultaneously, the security issues of such technology have become a focus of attention. There are many aspects of IoT security issues. In this paper, we propose a framework to solve the problem of network intrusion detection in IoT. First, an intrusion detection dataset named UNSW-NB15 is selected as the research object. Then, the dataset is preprocessed and the feature selection job is accomplished to obtain a suitable subset. After the above steps are completed, a Bayesian model is built according to the K2 structure learning algorithm. The parameters are obtained through the Maximum Likelihood Estimation algorithm. Finally, the testing dataset is inputted for classification. The simulation results show that the system can detect the anomaly intrusion effectively.

 

References: 28

                1. A. L. Buczak and E. Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications on Surveys & Tutorials, Vol. 18, No. 2, pp. 1153-1176, 2016
                2. N. Narendra, K. Ponnalagu, A. Ghose, and S. Tamilselvam, “Goal-Driven Context-Aware Data Filtering in IoT-based Systems,” in Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp. 2172-2179, 2015
                3. E. J. Derrick, R. W. Tibbs, and L. L. Reynolds, “Investigating New Approaches to Data Collection, Management and Analysis for Network Intrusion Detection,” in Proceedings of Southeast Regional Conference ACM, pp. 283-287, 2007
                4. J. Pearl, “Fusion, Propagation, and Structuring in Belief Networks,” Elsevier Science Publishers Ltd., 1986
                5. N. Moustafa and J. Slay, “A Hybrid Feature Selection for Network Intrusion Detection Systems: Central points,” 2015
                6. A. Agrawal, S. Mohammed, and J. Fiaidhi, “Developing Data Mining Techniques for Intruder Detection in Network Traffic,” International Journal of Security & Its Applications, Vol. 10, No. 8, pp. 335-342, 2016
                7. N. Moustafa and J. Slay, “The Evaluation of Network Anomaly Detection Systems: Statistical Analysis of the UNSW-NB15 Data Set and the Comparison with the KDD99 Data Set,” Information Systems Security, Vol. 25, No. 1-3, pp.18-31, 2016
                8. C. Papaloukas, D. I. Fotiadis, A. Likas, and L. K. Michalis, “An Ischemia Detection Method based on Artificial Neural Networks,” Artificial Intelligence in Medicine, Vol. 24, No. 2, pp. 167-178, 2002
                9. M. Belouch, S. El, and M. Idhammad, “A Two-Stage Classifier Approach using RepTree Algorithm for Network Intrusion Detection,” International Journal of Advanced Computer Science & Applications, Vol. 8, No. 6, 2017
                10. R. Primartha and B. A. Tama, “Anomaly Detection using Random Forest: A Performance Revisited,” in Proceedings of International Conference on Data and Software Engineering, pp. 1-6, 2017
                11. H. Gharaee and H. Hosseinvand, “A New Feature Selection IDS based on Genetic Algorithm and SVM,” in Proceedings of International Symposium on Telecommunications, pp. 139-144, 2017
                12. N. Moustafa and J. Slay, “The Significant Features of the UNSW-NB15 and the KDD99 Data Sets for Network Intrusion Detection Systems,” International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security IEEE, 2017
                13. T. Janarthanan and S. Zargari, “Feature Selection in UNSW-NB15 and KDDCUP'99 Datasets,” in Proceedings of IEEE International Symposium on Industrial Electronics, pp. 1881-1886, 2017
                14. H. M. Anwer, M. Farouk, and A. Abdel-Hamid, “A Framework for Efficient Network Anomaly Intrusion Detection with Features Selection,” in Proceedings of 9th International Conference on Information and Communication Systems (ICICS), pp. 157-162, Irbid, 2018
                15. A. M. Alberti and D. Singh, “Internet of Things-Perspectives, Challenges and Opportunities - Presentation Slides,” International Workshop on Telecommunications, 2013
                16. F. Gumus, C. O. Sakar, Z. Erdem, and O. Kursun, “Online Naive Bayes Classification for Network Intrusion Detection,” in Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 670-674, 2014
                17. W. Bul’Ajoul, A. James, and M. Pannu, “Improving Network Intrusion Detection System Performance through Quality of Service Configuration and Parallel Technology,” Journal of Computer & System Sciences, Vol. 81, No. 6, pp. 981-999, 2015
                18. J. Liu, “Bayesian Network Inference on Risks of Construction Schedule-Cost,” Information Science and Management Engineering, pp. 15-18, 2010
                19. P. Gogoi, M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Packet and Flow based Network Intrusion Dataset,” in Proceedings of International Conference on Contemporary Computing Springer Berlin Heidelberg, pp. 322-334, 2012
                20. M. Tavallaee, E. Bagheri, L. Wei, and A. A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” in Proceedings of IEEE International Conference on Computational Intelligence for Security & Defense Applications, pp. 1-6, 2009
                21. N. Moustafa and J. Slay, “UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set),” in Proceedings of Military Communications and Information Systems Conference, pp. 1-6, 2015
                22. A. H. Sung and S. Mukkamala, “Identifying Important Features for Intrusion Detection using Support Vector Machines and Neural Networks,” in Proceedings of Symposium on Applications and the Internet, pp. 209-216, 2003
                23. G. F. Cooper, “The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks (Research Note),” Elsevier Science Publishers Ltd., 1990
                24. U. Creutzig, et al., “A Framework for an Adaptive Intrusion Detection System using Bayesian Network,” Intelligence & Security Informatics, pp. 66-70, 2007
                25. G. F. Cooper and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, Vol. 9, No. 4, pp. 309-347, 1992
                26. M. C. Golumbic, “Algorithmic Graph Theory and Perfect Graphs,” Academic Press, 1980
                27. N. L. Zhang and D. Poole, “A Simple Approach to Bayesian Network Computations,” 1994
                28. K. Murphy, “The Bayes Net Toolbox for Matlab,” Computing Science & Statistics, pp. 33, 2001

                               

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