Volume 0 • Issue 1 • PP: 32-43 • 2019
Design of Optimal Machine Learning based Cybersecurity Intrusion Detection Systems
Open Access & Copyright
© 2019 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Cybersecurity is the process of protecting critical systems and confidential data from digital attacks. With the advent of machine learning, cybersecurity systems can examine the patterns and learns them from preventing similar attacks and responds to fluctuating behavior. Cybersecurity intrusion detection system helps to detect the existence of intrusions in the network and achieves security in confidential data storage and transmission. In this view, this study designs an efficient cockroach optimization (CSO) with kernel extreme learning machine (KELM) model for cybersecurity intrusion detection. The proposed CSO-KELM model can accomplish cybersecurity by the detection and classification of intrusions. The proposed CSO-KELM technique encompasses a three-level process, namely preprocessing, classification, and parameter tuning. The design of the CSO algorithm for the appropriate selection of KELM parameters results in improved classification performance. For examining the betterment of the CSO-KELM technique, a series of experiments were performed on benchmark datasets. The experimental results pointed out the superiority of the CSO-KELM technique concerning several measures.
Keywords
References
[1] Buczak, A.L. and Guven, E., 2015. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), pp.1153-1176.
[2] Abubakar, A.I., Chiroma, H., Muaz, S.A. and Ila, L.B., 2015. A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems. Procedia Computer Science, 62, pp.221-227.
[3] Myers, S.; Musacchio, J.; Bao, N. Intrusion Detection Systems: A Feature and Capability Analysis; Baskin School of Engineering: Santa Cruz, CA, USA, 2010.
[4] Mukkamala, S., Sung, A., Abraham, A. and Vemuri, V.R., 2005. Cyber security challenges: Designing efficient intrusion detection systems and antivirus tools. Vemuri, V. Rao, Enhancing Computer Security with Smart Technology.(Auerbach, 2006), pp.125-163.
[5] Abraham, A., Grosan, C. and Chen, Y., 2005. Cyber security and the evolution in intrusion detection systems. Journal of Engineering and Technology, ISSN, pp.0973-2632.
[6] Singh, P., Garg, S., Kumar, V. and Saquib, Z., 2015, August. A testbed for SCADA cyber security and intrusion detection. In 2015 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications (SSIC) (pp. 1-6). IEEE.
[7] Yang, Y., Xu, H.Q., Gao, L., Yuan, Y.B., McLaughlin, K. and Sezer, S., 2016. Multidimensional intrusion detection system for IEC 61850-based SCADA networks. IEEE Transactions on Power Delivery, 32(2), pp.1068-1078.
[8] Borkar, A., Donode, A. and Kumari, A., 2017, November. A survey on Intrusion Detection System (IDS) and Internal Intrusion Detection and protection system (IIDPS). In 2017 International conference on inventive computing and informatics (ICICI) (pp. 949-953). IEEE.
[9] Wu, W., Huang, Y., Kurachi, R., Zeng, G., Xie, G., Li, R. and Li, K., 2018. Sliding window optimized information entropy analysis method for intrusion detection on in-vehicle networks. IEEE Access, 6, pp.45233-45245.
[10] Vigneswaran, R.K., Vinayakumar, R., Soman, K.P. and Poornachandran, P., 2018, July. Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security. In 2018 9th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE.
[11] Gupta, G.P. and Kulariya, M., 2016. A framework for fast and efficient cyber security network intrusion detection using apache spark. Procedia Computer Science, 93, pp.824-831.
[12] Alom, M.Z. and Taha, T.M., 2017, June. Network intrusion detection for cyber security using unsupervised deep learning approaches. In 2017 IEEE national aerospace and electronics conference (NAECON) (pp. 63-69). IEEE.
[13] Wong, K., Dillabaugh, C., Seddigh, N. and Nandy, B., 2017, April. Enhancing Suricata intrusion detection system for cyber security in SCADA networks. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-5). IEEE.
[14] Lin, F., Zhou, Y., An, X., You, I. and Choo, K.K.R., 2018. Fair resource allocation in an intrusion-detection system for edge computing: Ensuring the security of Internet of Things devices. IEEE Consumer Electronics Magazine, 7(6), pp.45-50.
[15] Iosifidis, A., Tefas, A. and Pitas, I., 2015. On the kernel extreme learning machine classifier. Pattern Recognition Letters, 54, pp.11-17.
[16] Liu, X., Wang, L., Huang, G.B., Zhang, J. and Yin, J., 2015. Multiple kernel extreme learning machine. Neurocomputing, 149, pp.253-264.
[17] Lu, H., Du, B., Liu, J., Xia, H. and Yeap, W.K., 2017. A kernel extreme learning machine algorithm based on improved particle swam optimization. Memetic Computing, 9(2), pp.121-128.
[18] Kwiecień, J. and Pasieka, M., 2017. Cockroach swarm optimization algorithm for travel planning. Entropy, 19(5), p.213.
[19] Obagbuwa, I.C. and Abidoye, A.P., 2016. Binary cockroach swarm optimization for combinatorial optimization problem. Algorithms, 9(3), p.59.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.