An Efficient Weighted Trust-Based Malicious Node Detection Scheme for Wireless Sensor Networks

The aim of wireless sensor networks (WSNs) is to gather sensor data from a monitored environment. However, the collected or reported information might be falsified by faults or malicious nodes. Hence, identifying malicious nodes in an effective and timely manner is essential for the network to function properly and reliably. Maliciously behaving nodes are usually detected and isolated by reputation and trust-based schemes before they can damage the network. In this thesis, we propose an efficient weighted trust-based malicious node detection (WT-MND) scheme that can detect malicious nodes in a clustered WSN. The node behaviors are realistically treated by accounting for false-positive and false-negative instances. The simulation results confirm the timely identification and isolation of maliciously behaving nodes by the WT-MND scheme. The effectiveness of the proposed scheme is afforded by the adaptive trust-update process, which implicitly performs trust recovery of temporarily malfunctioning nodes and computes a different trust-update factor for each node depending on its behavior. The proposed scheme is more effective and scalable than the related schemes in the literature, as evidenced by its higher detection ratio (DR) and lower misdetection ratio (MDR), which only slightly vary with the network’s size. Moreover, the scheme sustains its efficient characteristics without significant power consumption overheads. Keywords: wireless sensor networks, network security, malicious node, weighted trust, intrusion detection system, LEACH protocol.