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

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

PDF

___

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.
S. Han, M. Xie, H. Chen and Y. Ling, "Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges," in IEEE Systems Journal, vol. 8, no. 4, pp. 1052-1062, Dec. 2014. doi: 10.1109/JSYST.2013.2257594
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.
E. Aydemir, "Weka ile Yapay Zekâ", Seckin, p:216, July 2018
P. I. Radoglou-Grammatikis and P. G. Sarigiannidis, "Securing the Smart Grid: A Comprehensive Compilation of Intrusion Detection and Prevention Systems," in IEEE Access, vol. 7, pp. 46595-46620, 2019. doi: 10.1109/ACCESS.2019.2909807
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.
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)
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
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
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.
O. Can and O. K. Sahingoz, "An intrusion detection system based on neural network," 2015 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, 2015, pp. 2302-2305. doi: 10.1109/SIU.2015.7130338
G. Karatas and O. K. Sahingoz, "Neural network based intrusion detection systems with different training functions," 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, 2018, pp. 1-6. doi: 10.1109/ISDFS.2018.8355327
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.
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.
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.
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.
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
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.
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.
A.K. Jain, “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, 2010, vol. 31, no. 8, pp. 651-666.
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.
G. Karatas, O. Demir and O. K. 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