Volume 14 • Issue 2 • PP: 25-35 • 2025
Intrusion Detection System in Wireless Sensor Networks Using Machine Learning
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Current industrial control systems are increasingly integrating with corporate Internet technology networks in order to fully utilize the abundant resources available on the Internet. The growing connection between industrial control systems and the internet has made them a desirable choice. Industrial control systems are in need of significant protection due to being a common target for a range of cyber-attacks. The use of the Internet of Things is currently increasing across industries due to its efficiency, and the Internet of Things is facing a security challenge. This document gives an overview of the intrusion detection system and the methods of the intrusion detection system. The purpose of this document is to examine intrusion detection methods and present the best method based on studies. Experimental results show that this system uses a combination of machine learning methods for high performance.
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References
[1] Z.A. Baig, S.M. Sait, A. Shaheen, GMDH-based networks for intelligent intrusion detection, Eng. Appl. Artif. Intell. 26 (7) (2013) 1731–1740.
[2] E. de la Hoz, E. de la Hoz, A. Ortiz, J. Ortega, A. Martinez-Alvarez, Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps, Knowl.-Based Syst. 71 (2014) 322–338.
[3] W. Feng, Q. Zhang, G. Hu, J.X. Huang, Mining network data for intrusion detection through combining SVMs with ant colony networks, Future Gener.Comput. Syst. 37 (2014) 127–140
[4] Zhou, W., et al., Detection and defense of application-layer DDoS attacks in backbone web traffic. Future Generation Computer Systems, 2014. 38: p. 36-46.
[5] Yu, S., Distributed denial of service attack and defense. 2014: Springer.
[6] Ussath, M., D. Jaeger, and F. Cheng, Identifying Suspicious User Behavior with Neural Networks. IEEE, 2017. 5(17).
[7] Choi, J., et al., A method of DDoS attack detection using HTTP packet pattern and rule engine in cloud computing environment. Soft Computing, 2014. 18(9): p. 1697-1703.
[8] Hodo, E., Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System. IEEE, 2016.
[9] KAWAMURA, T., et al., An NTP-based Detection Mo1d ule for DDoS Attacks on IoT. IEEE, 2017(978-1-5090-4017-9/17).
[10] Bahnasawi, M.A., et al. ASIC-oriented comparative review of hardware security algorithms for internet of things applications. in 2016 28th International Conference on Microelectronics (ICM). 2016. IEEE.
[11] da Silva Cardoso, A.M., et al. Real-Time DDoS Detection Based on Complex Event Processing for IoT. in Internet-of-Things Design and Implementation (IoTDI), 2018 IEEE/ACM Third International Conference on. 2018. IEEE.
[12] Xu, C., et al., An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units. IEEE Access, 2018. 6: p. 48697-48707.
[13] Yin, C., et al., A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 2017. 5: p. 21954-21961.
[14] Nayaki, R.S. and A.S. Kumar, An Analysis of DDoS Attack Detection and Mitigation Using Machine Learning System. International Journal on Recent and Innovation Trends in Computing and Communication, 2017. 5(10): p. 80-82.
[15] Diro, A.A. and N. Chilamkurti, distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 2018. 82: p. 761-768.
[16] Kasongo, S. M. and Y. Sun (2020). "A deep learning method with wrapper based feature extraction for wireless intrusion detection system." Computers & Security 92: 101752.
[17] Jin, D., et al. (2020). "SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism." Computers & Security 97: 101984.
[18] Kunhare, N., Tiwari, R. & Dhar, J. Particle swarm optimization and feature selection for intrusion detection system. Sādhanā 45, 109 (2020). https://doi.org/10.1007/s12046-020-1308-5
[19] Meryem, A. and B. E. L. Ouahidi (2020). "Hybrid intrusion detection system using machine learning." Network Security 2020(5): 8-19.
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