An Intelligent Intrusion Detection Framework for Wireless Sensor Networks Using Machine Learning

Intrusion Detection system protects modern cybersecurity networks through continuous traffic and system activity inspection for identifying potential security threats. Such systems provide organizations with the ability to find and respond instantly to unauthorized access and cyber threats and policy violations. The security of contemporary cybersecurity systems heavily depends on IDS for WSN and ad hoc Wireless Sensor Networks specifically require IDS because traditional security measures fail due to network decentralization. This research paper investigates current intrusion detection approaches designed for ad hoc WSNs while focusing on the distinct security threats DoS, Sybil, and black-hole attacks. The paper examines data gathering strategies and detection methods for IDS models alongside their performance evaluation in WSN environments with limited resources to establish ways for algorithm optimization between security needs and energy efficiency. The recent research should focus on enhancing IDS security while establishing Machine learning methods for behavioral analytics in wireless systems SVM shows best performance as compared to others.