Downloads: 1 | Views: 103 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1
Research Paper | Computer Engineering | India | Volume 12 Issue 9, September 2023
Intrusion Detection in Wireless Sensor Networks using Support Vector Machine with Grasshopper Optimization Algorithm
C. Murugesh | Dr. S. Murugan [2]
Abstract: Wireless Sensor Networks (WSNs) serve as the backbone for numerous critical applications, including environmental monitoring, industrial automation, and healthcare. However, the inherent vulnerabilities of these networks to security threats necessitate robust Intrusion Detection Systems (IDS). Traditional rule-based IDSs often fall short of addressing the evolving nature of attacks in WSNs. This research presents an innovative approach that combines the power of Support Vector Machine (SVM) classifiers with the Grasshopper Optimization Algorithm (GOA) for effective intrusion detection (SVMGOA-ID) technique in WSNs.The SVMGOA-ID approach harnesses SVM, a well-established machine learning technique known for its proficiency in binary classification tasks. SVMs are trained to distinguish between normal network behavior and intrusion attempts, learning intricate patterns from a labeled dataset. However, the success of SVMs is highly dependent on appropriate parameter settings, and suboptimal choices can lead to reduced detection accuracy.To address this challenge, the Grasshopper Optimization Algorithm (GOA) simulated by the natural behavior of grasshoppers in search of optimal foraging spots, is introduced for parameter optimization. The GOA efficiently explores the parameter space of SVM models, seeking the ideal configuration that maximizes intrusion detection accuracy. Comprehensive experiments are conducted using benchmark datasets, evaluating the efficiency of the SVMGOA-IDmethodology in detecting various intrusion types.
Keywords: Wireless Sensor Networks;Support Vector Machine; Intrusion Detection Systems; Grasshopper Optimization Algorithm
Edition: Volume 12 Issue 9, September 2023,
Pages: 133 - 140