A review on Machine Learning based IDS approaches in Wireless sensor networks

In wireless sensor networks (WSNs), sensors are scattered in a particular region to record and evaluate the physical data from the environment. They are widely utilised in various applications, including healthcare, event detection, agriculture, and disaster management. However, the challenges in WSNs include high bandwidth demand, energy consumption, route design, quality of service (QoS) provisioning, unusual activities, etc. However, numerous ways are being considered to solve the aforementioned difficulties. Detecting abnormal network movements is one of the most difficult challenges. An intrusion detection system (IDS) is a productive way to detect infiltration effectively. However, the growth of contemporary technologies necessitates using an effective IDS strategy. As a result, many academicians focus on reducing and eliminating anomalous network security vulnerabilities. According to the findings, machine learning techniques are more critical in addressing these issues. The accuracy of several machine learning algorithms for detecting intrusion in WSNs is investigated in this work. Furthermore, this paper examines the various datasets, performance measurements, and future directions.