Trusted head node for Node Behaviour Analysis for malicious node detection in wireless sensor networks

There has been a rise in the use of Wireless Sensor Networks (WSNs) in several fields. Depending on the malicious actions, attacks on WSN can take many forms. This research demonstrates how to leverage request and response data from each node to pinpoint the location of malicious nodes. If a node is malevolent, it will prohibit data from being sent to other nodes. They do an excellent job of pretending to be someone else. To enhance route quality, packet dropping can be used to perform a Denial of Service (DoS) assault by rejecting data packets and cutting them off from their destinations. The primary goal of this research is to expose malicious nodes within the network. To avoid being attacked by the false information fed by the adversary through compromised nodes, it is crucial to discover and isolate the nodes that have been compromised. Unfortunately, flat topology networks are difficult to keep safe because of their limited scalability and substantial communication overhead. By computing the packet drop and throughput values with valid nodes, efficient security architecture for the WSN can be proposed, allowing for the detection of malicious activity. If sensor networks are susceptible to attacks, they will be unable to fulfill their duty in a variety of event detection applications. Malicious nodes can cause unacceptable drops in event detection effectiveness and spikes in false alarm rates by producing fake readings and deceptive information. In this research, a method for identifying malicious nodes in WSNs is proposed. This research considers a Trusted Head Node (THN) for monitoring the nodes behaviour in the network to detect the malicious actions in the network. This research proposes a Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection (THN-NBA-MND) in WSN. The proposed model based on node behaviour analysis in frequent time intervals; detect the malicious nodes to improve the network performance in data transmission. The proposed model when contrasted with the existing model performs better in malicious node detection. © 2024