Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT
Subject Areas : Computer Engineering
Sepehr Sharifi
1
,
Soulmaz Gheisari
2
1 - Department of Information Technology ,Science and Research Branch, Islamic Azad university, Tehran, Iran
2 - Department of computer engineering, Pardis Branch, Islamic Azad University, Pardis, Iran
Keywords: grasshopper optimization algorithm, IoT, Support vector machine, anomaly-based intrusion detection,
Abstract :
Computer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, network nodes can be smart objects, and in this sense, this network has many nodes and there is a lot of traffic in this network. Like any computer network, it faces its own challenges and problems, one of which is the issue of network intrusion and disruption. This dissertation focuses on detecting anomaly-based intrusion into the Internet of Things using data mining. In this study, after collecting and preparing data, the improved support vector machine with grasshopper optimization algorithm is used as a proposed method to detect anomaly-based intrusion in the Internet of Things. The bagging and k-nearest neighbor classifiers and Basic SVM are compared based on error types and standard performance criteria. The simulation results show 97.2% accuracy in the proposed method and better performance compared to other methods.
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[10] Sh. Ghafarian and K. Rezaei and A. Kafash, ” A survey on intrusion detection approaches in IOT”, Third National Conference on Applied Research in Electrical, Computer and Medical Engineering,2019
[11] V. Kumar, A. K. Das, and D. Sinha, "UIDS: a unified intrusion detection system for IoT environment," Evolutionary Intelligence, vol. 14, no. 1, pp. 47-59, 2021, doi: 10.1007/s12065-019-00291-w.
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[1] A. J. Siddiqui and A. Boukerche, "TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things," Cluster Computing, vol. 24, no. 1, pp. 17-35, 2021, doi: 10.1007/s10586-020-03153-8.
[2] A. Khraisat and A. Alazab, "A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges," Cybersecurity, vol. 4, no. 1, pp. 1-27, 2021, doi: 10.1186/s42400-021-00077-7.
[3] B. S. Khater, A. Wahab, A. W. Bin, M. Y. I. B. Idris, M. A. Hussain, and A. A. Ibrahim, "A lightweight perceptron-based intrusion detection system for fog computing," Applied Sciences, vol. 9, no. 1, p. 178, 2019, doi: 10.3390/app9010178.
[4] J. Bard, “What Is Data Mining?” PowerKids Press, 2018.
[5] M. Roopak, G. Y. Tian and J. Chambers, "An Intrusion Detection System Against DDoS Attacks in IoT Networks," 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020, pp. 0562-0567, doi: 10.1109/CCWC47524.2020.9031206.
[6] M. Safaldin, M. Otair, and L. Abualigah, "Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks," Journal of ambient intelligence and humanized computing, vol. 12, no. 2, pp. 1559-1576, 2021, doi: 10.1007/s12652-020-02228-z.
[7] N. Huber, S. R. Kalidindi, B. Klusemann, and C. J. Cyron, ”Machine Learning and Data Mining in Materials Science,” Frontiers Media SA, 2020.
[8] N. Islam et al., "Towards machine learning based intrusion detection in IoT networks," Comput. Mater. Contin, vol. 69, pp. 1801-1821, 2021, doi: 10.32604/cmc.2021.018466.
[9] S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: Theory and application," Advances in Engineering Software, vol. 105, pp. 30-47, 2017, doi: 10.1016/j.advengsoft.2017.01.004.
[10] Sh. Ghafarian and K. Rezaei and A. Kafash, ” A survey on intrusion detection approaches in IOT”, Third National Conference on Applied Research in Electrical, Computer and Medical Engineering,2019
[11] V. Kumar, A. K. Das, and D. Sinha, "UIDS: a unified intrusion detection system for IoT environment," Evolutionary Intelligence, vol. 14, no. 1, pp. 47-59, 2021, doi: 10.1007/s12065-019-00291-w.
[12] X. S. Yang, “Introduction to Algorithms for Data Mining and Machine Learning”. Elsevier Science & Technology, 2019.
