AI- Based UAV with Fuzzy Logic Algorithm using Wormhole Attack Detection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a vital role in modern communication systems but remain highly vulnerable to sophisticated security threats—particularly wormhole attacks. These attacks exploit low-latency links between compromised nodes, disrupting routing protocols and often bypassing conventional cryptographic defenses. To address this challenge, this study proposes an AI-based Unmanned Aerial Vehicle (UAV) system integrated with a fuzzy logic algorithm to enhance the detection and mitigation of wormhole attacks in WSNs. The UAV acts as a mobile monitoring agent, dynamically analyzing communication patterns and network behaviors. Fuzzy logic enables the system to make intelligent decisions based on uncertain or imprecise inputs such as signal strength, hop count deviation, and packet delay. By incorporating energy-aware fuzzy clustering, the UAV optimizes its flight and monitoring tasks, extending network lifetime and improving detection accuracy. This research not only compares existing wormhole detection techniques but also introduces a novel, adaptive, and energy-efficient approach using AI and UAV technology to safeguard WSNs from one of their most dangerous attack vectors.