A Clustering Approach for Intrusion Detection with Big Data Processing on Parallel Computing Platform

Abstract

Abstract— In recent years there is a growing number of attacks in the computer networks. Therefore, the use of a prevention mechanism is an inevitable need for security admins. Although firewalls are preferred as the first layer of protection, it is not sufficient for preventing lots of the attacks, especially from the insider attacks. Intrusion Detection Systems (IDSs) have emerged as an effective solution to these types of attacks. For increasing the efficiency of the IDS system, a dynamic solution, which can adapt itself and can detect new types of intrusions with a dynamic structure by the use of learning algorithms is mostly preferred. In previous years, some machine learning approaches are implemented in lots of IDSs. In the current position of artificial intelligence, most of the learning systems are transferred with the use of Deep Learning approaches due to its flexibility and the use of Big Data with high accuracy. In this paper, we propose a clustered approach to detect the intrusions in a network. Firstly, the system is trained with Deep Neural Network on a Big Data set by accelerating its performance with the use of CUDA architecture. Experimental results show that the proposed system has a very good accuracy rate and low runtime duration with the use of this parallel computation architecture. Additionally, the proposed system needs a relatively small duration for training the system 

Keywords

• Deep Neural Network,Big Data,CUDA,GPU,Parallel Computation

References

1. [1] A. Borkar, A. Donode, and A. Kumari, "A survey on Intrusion Detection System (IDS) and Internal Intrusion Detection and protection system (IIDPS)," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 949-953.
2. [2] L. Haripriya and M. A. Jabbar, "Role of Machine Learning in Intrusion Detection System: Review," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2018, pp. 925-929. doi: 10.1109/ICECA.2018.8474576
3. [3] F. Farahnakian and J. Heikkonen, “A deep auto-encoder based approach for intrusion detection system,” in Advanced Communication Technology (ICACT), 2018 20th International Conference on. IEEE, 2018, pp. 178–183.
4. [4] E.H. Spafford, D. Zamboni, “Intrusion Detection Using Autonomous Agents. Computer Networks”, The International Journal of Computer and Telecommunications Networking 34 (4), 547–570 (2000)
5. [5] H. Sagha, S. B. Shouraki, H. Khasteh and M. Dehghani, "Real-Time IDS Using Reinforcement Learning," 2008 Second International Symposium on Intelligent Information Technology Application, Shanghai, 2008, pp. 593-597.doi: 10.1109/IITA.2008.512
6. [6] M. A. Jabbar and S. Samreen, "Intelligent network intrusion detection using alternating decision trees," 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), Bangalore, 2016, pp. 1-6.doi: 10.1109/CIMCA.2016.8053265
7. [7] A. S. Desai and D. P. Gaikwad, "Real time hybrid intrusion detection system using signature matching algorithm and fuzzy-GA," 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), Pune, 2016, pp. 291-294.
8. [8] C.F. Tsai, Y.F. Hsu, C.-Y. Lin, W.-Y. Lin, “Intrusion detection by machine learning: A review”, Expert Systems with Applications, 2009, vol. 36, no. 10, pp. 11994-120000.

9. [9] W.C. Lin, S.W. Ke, C.F. Tsai, “CANN: An Intrusion Detection System Based on Combining Cluster Centers and Nearest Neighbors”, Knowledge-Based Systems, 2015, vol. 78, pp. 13-21.
10. [10] J. Han, Kamber M., Pei J.; Data Mining: Concepts and Techniques, 3rd edn., Morgan Kaufmann Publishers, 2011, Massachusetts, ISBN 9780123814791
11. [11] X. H. Cao, I. Stojkovic and Z. Obradovic, “A robust data scaling algorithm to improve classification accuracies in biomedical data”, BMC Bioinformatics, 2016, vol. 17, no. 1, pp. 359.
12. [12] N. Paulauskas and J. Auskalnis, “Analysis of Data Pre-Processing Influence on Intrusion Detection Using NSL-KDD Dataset”, 2017 Open Conference of Electrical, Electronic and Information Sciences (eStream), 2017, Vilnius, pp. 1-5.
13. [13] S. Zaman and F. Karray, “Features Selection for Intrusion Detection Systems Based on Support Vector Machines”, 2009 6th IEEE Consumer Communications and Networking Conference, 2009, Las Vegas, NV, pp. 1-8.
14. [14] O. Y. Al-Jarrah, A. Siddiqui, M. Elsalamouny, P. D. Yoo, S. Muhaidat and K. Kim, “Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection”, 2014 IEEE 34th International Conference on Distributed Computing Systems Workshops (ICDCSW), 2014, Madrid, pp. 177-181.
15. [15] Z. Elkhadir, K. Chougdali and M. Benattou, “Intrusion Detection System Using PCA and Kernel PCA Methods”, In: El Oualkadi A., Choubani F., El Moussati A. (eds) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol. 381, Springer.
16. [16] D. Tanikić and V. Despotovic, “Artificial Intelligence Techniques for Modelling of Temperature in the Metal Cutting Process”, 2012, Metallurgy, Yogiraj Pardhi, IntechOpen, DOI 10.5772/47850
17. [17] J. S. R. Jang, C. T. Sun and E. Mizutani, “Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]” in IEEE Transactions on Automatic Control, 1997, vol. 42, no. 10, pp. 1482-1484.
18. [18] J. B. MacQueen, "Some Methods for classification and Analysis of Multivariate Observations," in 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, 1967, pp. 281-297.
19. [19] T. Velmurugan and T. Santhanam, "Performance Evaluation of K-Means and Fuzzy C-Means Clustering Algorithms for Statistical Distributions of Input Data Points" European Journal of Scientific Research, vol. 46, no. 3, pp. 320-330, 2010.
20. [20] A. Singh, A. Yadav & A. Rana, “K-means with Three Different Distance Metrics”, International Journal of Computer Applications, 2013, vol. 67, no. 10, pp. 13-17.
21. [21] A.K. Jain, “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, 2010, vol. 31, no. 8, pp. 651-666.
22. [22] D. Y. Mahmood, M. A. Hussein, “Feature Based Unsupervised Intrusion Detection”, International Journal of Computer and Information Engineering, 2014, vol. 8, no. 9, pp. 1665-1669.
23. [23] B. Ingre and A. Yadav, “Performance analysis of NSL-KDD dataset using ANN”, 2015 International Conference on Signal Processing and Communication Engineering Systems (SPACES), 2015, pp. 92-96.
24. [24] S. A. Ludwig, "Intrusion detection of multiple attack classes using a deep neural net ensemble," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017, pp. 1-7.
25. [25] G. Karatas, O. Demir and O. Koray Sahingoz, "Deep Learning in Intrusion Detection Systems," 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Ankara, Turkey, 2018, pp. 113-116. doi: 10.1109/IBIGDELFT.2018.8625278