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


  • 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.


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


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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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



Research Article