An Exploratory Machine Learning Technique for Investigating Intrusion in Wireless Sensor Networks
Bhimaraya Patil, Jayashree V. Agarkhed · 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC) · 2020
Intrusion Detection System (IDS) is an efficient defense method against cyber-attacks in Wireless Sensor Networks (WSNs). A Novel paradigm of the Machine Learning technique called the Radial Bias technique has emerged recently for the improved detection rate and reduced false positives of intrusion over existing decision tree techniques. Malicious activities used in this work are Sleep Deprivation and Sinkhole attacks. Radial Basis Function (RBF) classifiers can detect novel attack types. The Radial Bias Function is used for timely and accurate detection of malicious activities. In this work, the performance of the Proposed Intrusion Detection System has been recorded in terms of an empirical result. The proposed RBF system's empirical results are more accurate than decision tree techniques.