Volume 12, Issue 3 (Journal of Control, V.12, N.3 Fall 2018)                   JoC 2018, 12(3): 63-75 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

ghazi Z, Doustmohammadi A. Cyber intrusion detection on critical infrastructures using fuzzy neural first order hybrid Petri net . JoC 2018; 12 (3) :63-75
URL: http://joc.kntu.ac.ir/article-1-412-en.html
1- Amirkabir university
Abstract:   (7389 Views)

Due to the growing demand to achieve more secure and reliable systems, development of models, analysis and design of appropriate procedures seems to be necessary. The aim of this paper is designing a controller in order to detect cyber intrusion. In this paper fuzzy neural first order hybrid Petri net is used to design a controller that is capable of detecting cyber intrusions accurately as soon as possible. The stability of the proposed intrusion detection system has been proven for any network conditions and input parameters. To evaluate controller performance, DARPA standard data set is used. The simulation results confirm proper detection rate, low of false positive rate, and also surprisingly high convergence speed.

Full-Text [PDF 1565 kb]   (5075 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2016/10/13 | Accepted: 2018/02/12 | Published: 2019/04/28

1. [1] M. Govindarasu, A. Hahn, P. Sauer, Cyber-Physical Systems Security for Smart Grid, Future Grid Initiative White Paper, PSERC Publication, May 2012.
2. [2] Depren, Ozgur, et al. "An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks." Expert systems with Applications 29.4, pp. 713-722, 2005. [DOI:10.1016/j.eswa.2005.05.002]
3. [3] J.P. Anderson, Computer security threat monitoring and surveillance, Technical Report, James P. Anderson Co., Fort Washington, PA, 1980.
4. [4] Liao, Hung-Jen, et al, Intrusion detection system: A comprehensive review, Journal of Network and Computer Applications, 36.1, pp. 16-24, 2013. [DOI:10.1016/j.jnca.2012.09.004]
5. [5] Modi, Chirag, et al, A survey of intrusion detection techniques in cloud, Journal of Network and Computer Applications, 36.1, pp. 42-57, 2013. [DOI:10.1016/j.jnca.2012.05.003]
6. [6] Dagar, Vishwajeet, Vatsal Prakash, and Tarunpreet Bhatia. "Analysis of pattern matching algorithms in network intrusion detection systems." Advances in Computing, Communication, & Automation (ICACCA)(Fall), International Conference on. IEEE, 2016. [DOI:10.1109/ICACCAF.2016.7748969]
7. [7] S. Antonatos, K.G. Anagnostakis, and E.P. Markatos, Generating realistic workloads for network intrusion detection systems, ACM SIGSOFT Software Engineering Notes 29, no. 1, 207215, 2004. [DOI:10.1145/974043.974078]
8. [8] Gharaee, Hossein, Shokoufeh Seifi, and Nima Monsefan. "A survey of pattern matching algorithm in intrusion detection system." Telecommunications (IST), 2014 7th International Symposium on. IEEE, 2014. [DOI:10.1109/ISTEL.2014.7000839]
9. [9] Sahasrabuddhe, Atmaja, et al. "Survey on Intrusion Detection System using Data Mining Techniques.", 2017.
10. [10] Buczak, Anna L., and Erhan Guven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications Surveys & Tutorials 18.2, pp. 1153-1176, 2016. [DOI:10.1109/COMST.2015.2494502]
11. [11] Denatious, D. K., John, A., Survey on data mining techniques to enhance intrusion detection, In Computer Communication and Informatics (ICCCI), International Conference IEEE, pp.1-5, 2012. [DOI:10.1109/ICCCI.2012.6158822]
12. [12] Kshirsagar, Vivek K., Sonali M. Tidke, and Swati Vishnu, Intrusion Detection System using Genetic Algorithm and Data Mining: An Overview, International Journal of Computer Science and Informatics ISSN (PRINT), pp. 2231-5292, 2012.
13. [13] Goyal, Mayank Kumar, and Alok Aggarwal, composing signatures for misuse intrusion detection system using genetic algorithm in an offline environment, Advances in Computing and Information Technology, Springer Berlin Heidelberg, pp. 151-157, 2012. [DOI:10.1007/978-3-642-31513-8_16]
14. [14] Desai, Anuja S., and D. P. Gaikwad. "Real time hybrid intrusion detection system using signature matching algorithm and fuzzy-GA." Advances in Electronics, Communication and Computer Technology (ICAECCT), IEEE International Conference on, 2016. [DOI:10.1109/ICAECCT.2016.7942601]
15. [15] S. Kumar, Classification and detection of computer intrusions, Ph.D. thesis, Purdue University, 1995.
16. [16] Dolgikh, A., Nykodym, T., Skormin, V., Antonakos, J., Baimukhamedov, M., Colored Petri nets as the enabling technology in intrusion detection systems, In MILITARY COMMUNICATIONS CONFERENCE IEEE, pp. 1297-1301, 2011. [DOI:10.1109/MILCOM.2011.6127481]
17. [17] Helmer, Guy, et al, Software fault tree and coloured Petri netbased specification, design and implementation of agent-based intrusion detection systems, International Journal of Information and Computer Security, 1.1, pp. 109-142, 2007. [DOI:10.1504/IJICS.2007.012246]
18. [18] Horng, Shi-Jinn, et al, A novel intrusion detection system based on hierarchical clustering and support vector machines, Expert systems with Applications, 38.1, pp. 306-313, 2011. [DOI:10.1016/j.eswa.2010.06.066]
19. [19] Ahmed, Mohiuddin, Abdun Naser Mahmood, and Jiankun Hu. "A survey of network anomaly detection techniques." Journal of Network and Computer Applications 60, pp. 19-31, 2016. [DOI:10.1016/j.jnca.2015.11.016]
20. [20] XU, Yan-qun, Bin ZHANG, and Xiao-tie QIN, Clustering intrusion detection model based on grey fuzzy K-mean clustering, Journal of Chongqing Normal University (Natural Science), 1, 019, 2013.
21. [21] Pandeeswari, N., and Ganesh Kumar. "Anomaly detection system in cloud environment using fuzzy clustering based ANN." Mobile Networks and Applications 21.3, pp. 494-505, 2016. [DOI:10.1007/s11036-015-0644-x]
22. [22] Goh, Jonathan, et al. "Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks." High Assurance Systems Engineering (HASE), IEEE 18th International Symposium on., 2017. [DOI:10.1109/HASE.2017.36]
23. [23] C. Bitter, J. North, D. A. Elizondo, T. Watson, An Introduction to the Use of Neural Networks for Network Intrusion Detection, Computational Intelligence for Privacy and Security, Springer-Verlag Berlin Heidelberg, SCI 394, 524, 2012. [DOI:10.1007/978-3-642-25237-2_2]
24. [24] Roy, Sanjiban Sekhar, et al. "A Deep Learning Based Artificial Neural Network Approach for Intrusion Detection." International Conference on Mathematics and Computing. Springer, Singapore, 2017. [DOI:10.1007/978-981-10-4642-1_5]
25. [25] Ashfaq, Rana Aamir Raza, et al. "Fuzziness based semi-supervised learning approach for feed-forward neural network. In Computer intrusion detection system." Information Sciences 378, pp. 484-497, 2017. [DOI:10.1016/j.ins.2016.04.019]
26. [26] Li, Wei, Using genetic algorithm for network intrusion detection, Proceedings of the United States Department of Energy Cyber Security Group, pp. 1-8, 2004.
27. [27] Srinivasu, P., and P. S. Avadhani, Genetic Algorithm based Weight Extraction Algorithm for Artificial Neural Network Classifier in Intrusion Detection, Procedia Engineering, 38, pp. 144-153, 2012. [DOI:10.1016/j.proeng.2012.06.021]
28. [28] Lu, Wei, and Ali A. Ghorbani, Network anomaly detection based on wavelet analysis, EURASIP Journal on Advances in Signal Processing, 4, 2009. [DOI:10.1155/2009/837601]
29. [29] Aburomman, Abdulla Amin, and Mamun Bin Ibne Reaz. "A novel SVM-kNN-PSO ensemble method for intrusion detection system." Applied Soft Computing 38, pp. 360-372, 2016. [DOI:10.1016/j.asoc.2015.10.011]
30. [30] Ambusaidi, Mohammed A., et al. "Building an intrusion detection system using a filter-based feature selection algorithm." IEEE transactions on computers 65.10, pp. 2986-2998, 2016. [DOI:10.1109/TC.2016.2519914]
31. [31] G. Helmer, J. Wong, M. Slagell,V. Honavar, L. Miller, Y. Wang,X. Wang and N. Stakhanova, Software fault tree and coloured Petri net-based specification, design and implementation of agent-based intrusion detection systems, Int. J. Information and Computer Security, Vol. 1, No. 1/2, 2007. [DOI:10.1504/IJICS.2007.012246]
32. [32] C. Wooi Ten, C. Ching Liu, and M. Govindarasu, Vulnerability Assessment of Cybersecurity for SCADA Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, 2008.
33. [33] T. M. Chen, J. Carlos Sanchez-Aarnoutse, and J. Buford, Petri Net Modeling of Cyber-Physical Attacks on Smart Grid, IEEE TRANSACTIONS ON SMART GRID, VOL. 2, NO. 4, 2011. [DOI:10.1109/TSG.2011.2160000]
34. [34] Heracleous, Constantinos, et al. "Hybrid systems modeling for critical infrastructures interdependency analysis." Reliability Engineering & System Safety 165, pp. 89-101, 2017. [DOI:10.1016/j.ress.2017.03.028]
35. [35] Ghazi, Z., and A. Doustmohammadi. "Fault detection and power distribution optimization of smart grids based on hybrid Petri net." Energy Systems 8.3, pp. 465-493, 2017. [DOI:10.1007/s12667-016-0205-9]
36. [36] Petri, C.A., Kommunikation mit Automaten. Bonn: Institut für Instrumentelle Mathematik, Schriften des IIMNr. 2, 1962.
37. [37] David, R. and Alla, H.,, Continuous Petri nets. 8th European Workshop on Application and Theory of Petri Nets Zaragoza, 1987.
38. [38] Le Bail, J., Alla, H., and David, R. Hybrid Petri nets. European Control Conference Grenoble, pp. 1472-1477, 1991.
39. [39] G.W. Brams, Réseaux de Petri, Vol I et II, Masson, Paris, 1983.
40. [40] P. J. Hawrylak, M. Haney, M. Papa, and J. Hale, Using Hybrid Attack Graphs to Model Cyber-Physical Attacks in the Smart Grid, IEEE 2012. [DOI:10.1109/ISRCS.2012.6309311]
41. [41] T. Murata, Petri nets: properties, analysis and applications, Proceedings IEEE, vol.77, no. 4, pp 541-580, 1989. [DOI:10.1109/5.24143]
42. [42] F. Balduzzi, A. Giua, and G. Menga, First-Order Hybrid Petri Nets: A Model for Optimization and Control, IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 16.4, pp. 382-399, 2000. [DOI:10.1109/70.864231]
43. [43] Wai, Rong-Jong, and Chia-Ming Liu., Design of dynamic petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot, IEEE transactions on Industrial Electronics 56, no.7, pp. 2667-2683, 2009. [DOI:10.1109/TIE.2009.2020077]
44. [44] http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
45. [45] Tavallaee, Mahbod, et al, A detailed analysis of the KDD CUP 99 data set, Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications, 2009. [DOI:10.1109/CISDA.2009.5356528]
46. [46] Ye, Yalan, et al, A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction, Applied Mathematics and Computation, 205.2, pp. 799-806, 2008. [DOI:10.1016/j.amc.2008.05.117]
47. [47] Mitchell, Robert, and Ing-Ray Chen. "A survey of intrusion detection techniques for cyber-physical systems." ACM Computing Surveys (CSUR) 46.4: 55, 2014. [DOI:10.1145/2542049]
48. [48] Buczak, Anna L., and Erhan Guven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications Surveys & Tutorials 18.2, pp. 1153-1176, 2016. [DOI:10.1109/COMST.2015.2494502]
49. [49] Z. Ghazi, A. Doustmohammadi, Intrusion detection in cyber-physical systems based on Petri net, accepted in journal of information technology and control.
50. [50] Haddadi, F., Khanchi, S., Shetabi, M., & Derhami, V. (2010, April). Intrusion detection and attack classification using and Network Technology (ICCNT), pp. 262-266, IEEE 2010. [DOI:10.1109/ICCNT.2010.28]
51. [51] Z. Chunlin, J. Ju, K. Mohamed, Intrusion detection using hierarchical neural networks, Pattern Recognition Lett. 26 (6), pp. 779-791, 2005. [DOI:10.1016/j.patrec.2004.09.045]
52. [52] Liu, Guisong, Zhang Yi, and Shangming Yang. "A hierarchical intrusion detection model based on the PCA neural networks." Neurocomputing 70.7, pp. 1561-1568, 2007. [DOI:10.1016/j.neucom.2006.10.146]
53. [53] Jawhar, Muna Mhammad T., and Monica Mehrotra. "Design network intrusion detection system using hybrid fuzzy-neural network." International Journal of Computer Science and Security 4.3, pp. 285-294, 2010.
54. [54] Balduzzi, Fabio, et al. "Decidability results in First-Order Hybrid Petri Nets." Discrete Event Dynamic Systems 11.1-2, pp. 41-57, 2001. [DOI:10.1023/A:1008383031624]
55. [55] Alan A. Desrochers and Robert Y. AI-Jaar, Applications of Petri Nets in Manufacturing Systems; Modeling, Control, and Performance Analysis IEEE Press, ISBN 0-87942-295-5, 1995.

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb