A Fuzzy-Enhanced Recursive Feature Elimination for Intrusion Detection In WSN
Jayaram Reddy Avulapalli, Ashwini B. Abhale · International Journal of Intelligent Engineering Informatics · 2024
This study addresses the critical need for enhanced security in wireless sensor networks (WSNs), which are integral to modern infrastructure yet highly vulnerable to security breaches. We introduce a ground-breaking intrusion detection system (IDS) that employs recursive feature elimination (RFE) alongside advanced fuzzy classifiers, providing a robust solution against these security threats. The proposed IDS uniquely integrates RFE with three distinct fuzzy classifiers: the adaptive neuro-fuzzy classifier (ANFC) with a 97% accuracy rate, the fuzzy nearest neighbour classifier (FNNC) with 92% accuracy, and the fuzzy decision tree classifier (FDTC) achieving an exceptional 98% accuracy. This innovative approach leverages the strength of RFE in feature selection and the sophisticated pattern recognition capabilities of fuzzy logic. It significantly enhances the system's ability to accurately differentiate between normal operations and potential security threats in WSNs. The effectiveness of this IDS is highlighted by its remarkable accuracy rates, which are a direct result of the focused analysis of critical attributes facilitated by the RFE process. This research contributes significantly to bolstering the integrity and security of WSNs, presenting a notable methodological advancement in intrusion detection technologies.