HYBRID MACHINE LEARNING TECHNIQUES FOR DETECTING INTRUSION DETECTION SYSTEMS AND ANALYSING THE TRUST-BASED WSN

Machine Learning can deliver real-time solutions that optimise network resource use, prolonging network lifetime. It can process autonomously without being programmed externally, making the process simpler, more efficient, less expensive, and more reliable. ML algorithms can process complex data more quickly and precisely. Machine Learning is being used to enhance the Wireless Sensor Network environment. Wireless Sensor Networks (WSN) comprise several decentralised and distributed networks by design. WSNs comprise sensor nodes and sink self-organizing nodes and self-healing. We proposed the HMIDS method to detect intrusion and analyze the trust-based WSN using hybrid machine learning algorithms. Since real datasets are inaccessible, most IoT intrusion detection research is predicated on the benchmarked KDD cup or simulated datasets. WSNs have grown dramatically in recent years due to electronics and wireless communication technology advancements. Yet, significant issues persist, such as low computational capability, limited memory, and limited energy supplies. To need source-based privacy measures, infrastructure must be physically susceptible. WSNs are used to monitor changing environments, and Machine Learning approaches are essential for sensor networks to adapt to this situation and avoid wasteful redesign. An analysis of machine learning methods for WSNs indicates a wide range of applications where security is highly valued. To secure data from hackers and other attackers, the WSN's system should be capable of erasing instructions if hackers or other attackers try to steal data.