Network Intrusion Detection System (NIDS) for WSN using Particle Swarm Optimization based Artificial Neural Network

Authors

  • Atul Srivastava Amity School of Engineering and Technology, AUUP, Lucknow.
  • Sushanth Chandra Addimulam Sr. Infrastructure and Security Engineer, Applied Computer Techniques 28345 Beck Road STE 308, Wixom, MI- 48393
  • M. Trinath Basu Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500075, Telangana, India
  • B. Prema Sindhuri Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, India
  • Rajesh Kumar Maurya Associate Professor, Department of Computer Applications, ABES Engineering College, 19th KM Stone, NH-9, Ghaziabad, UP 201009.

Keywords:

NIDS, PSO, ANN, KNN, decision trees, WSN, accuracy, sensitivity, specificity, M. Trinath Basu3

Abstract

This research presents a novel approach to enhancing the security of Wireless Sensor Networks (WSNs) through the integration of Particle Swarm Optimization (PSO) with an Artificial Neural Network (ANN) for effective Network Intrusion Detection Systems (NIDS). WSNs, characterized by resource constraints and dynamic operating environments, are susceptible to various security threats. The proposed system leverages PSO to optimize the parameters of the ANN, aiming to improve the accuracy and efficiency of intrusion detection. PSO facilitates the exploration of the ANN's parameter space, adapting the network to the unique characteristics of WSNs and enhancing its ability to discern normal from malicious network activities. This paper involves the design and implementation of the PSO-based ANN model, followed by comprehensive evaluations using real-world WSN datasets acquired from Kaggle. The results demonstrate the superior performance of the proposed NIDS in terms of accuracy, sensitivity, and specificity compared to traditional methods. The synergy between PSO and ANN contributes to a forceful and adaptive intrusion detection system tailored for the resource-constrained nature of WSNs. This research addresses the critical need for reliable security mechanisms in WSNs and establishes a foundation for further advancements in the intersection of optimization techniques and artificial intelligence for cybersecurity applications in wireless sensor environments. The result demonstrated the proposed method PSO + ANN outperformed KNN and decision trees.

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References

Arsalwad, G., More, S., Tiwari, A., Zade, R., & Kudale, K. (2023). Analyze And Forecast The Cyber Attacks Using Machine Learning Techniques. International Research Journal of Modernization in Engineering Technology and Science, 5(11), 2566-2569.

Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q., & Gasmi, K. (2023). Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity. Applied Sciences, 13(13), 7507.

Ponnusamy, V., Yichiet, A., Jhanjhi, N. Z., & Almufareh, M. F. (2022). IoT Wireless Intrusion Detection and Network Traffic Analysis. Computer Systems Science & Engineering, 40(3).

Jhaveri, R. H., Revathi, A., Ramana, K., Raut, R., & Dhanaraj, R. K. (2022). A review on machine learning strategies for real-world engineering applications. Mobile Information Systems, 2022.

Muruganandam, S., Salameh, A. A., Pozin, M. A. A., Manikanthan, S. V., & Padmapriya, T. (2023). Sensors and machine learning and AI operation-constrained process control method for sensor-aided industrial internet of things and smart factories. Measurement: Sensors, 25, 100668.

Liu, J., and Zhu, L. (2021). Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing. Cognitive Computing Solutions for Complexity Problems in Computational Social System, 2021, Article ID 5556651, 1-11.

Shreyanth, S. Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data.

Arshad, J., Azad, M. A., Mahmoud Abdellatif, M., Ur Rehman, M. H., & Salah, K. (2019). COLIDE: A collaborative intrusion detection framework for Internet of Things. IET Networks, 8(1), 3-14.

A.J. Malik, F.A. Khan. (2017). A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection. Cluster Comput., 21(2018), 667–680.

Mojtahedi, A., Sorouri, F., Souha, A. N., Molazadeh, A., & Mehr, S. S. (2022). Feature selection-based intrusion detection system using genetic whale optimization algorithm and sample-based classification. arXiv preprint arXiv:2201.00584.

Ramani, S., Rao, S. S., Latha, S., & Prasad, L. C. (2022). A Novel Artificial Fish Integrated Particle Swarm Optimization (AFIPSO) and Random Artificial Neural Network Combined Gradient Descent (RANN-GD) Algorithm for WSN Security. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 07-15.

Liu, G., Zhao, H., Fan, F., Liu, G., Xu, Q., & Nazir, S. (2022). An enhanced intrusion detection model based on improved kNN in WSNs. Sensors, 22(4), 1407.

Xu, H., Przystupa, K., Fang, C., Marciniak, A., Kochan, O., & Beshley, M. (2020). A combination strategy of feature selection based on an integrated optimization algorithm and weighted k-nearest neighbor to improve the performance of network intrusion detection. Electronics, 9(8), 1206.

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Published

07.02.2024

How to Cite

Srivastava, A. ., Addimulam, S. C. ., Basu, M. T. ., Sindhuri, B. P. ., & Maurya, R. K. . (2024). Network Intrusion Detection System (NIDS) for WSN using Particle Swarm Optimization based Artificial Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 143–150. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4726

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

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