Artificial Intelligence-based Anomaly Detection for Security Enhancement in Heterogeneous WSN Environments

Due to the decentralized nature and resource constraint of wireless sensor networks, the applications are prone to numerous security threats, such as denial of service, distributed denial of service attacks, node compromise attacks, and data tampering. These security challenges become more complex in heterogeneous wireless sensor network environments where sensor nodes have differing energy capacities and processing capabilities. For this purpose, an artificial intelligence-based anomaly detection system is introduced to enhance security in heterogeneous wireless sensor networks. The proposed system detects anomalies using machine learning and deep learning models, analyzing sensor data and real-time network metrics. The network behavior is continuously learned from during which the algorithm picks up the anomalies within the normal patterns; therefore, the offered solution is highly dynamic and adaptive for the evolving threat landscape. To maintain network longevity, the proposed methodology integrates an energy-efficient routing protocol that reduces energy consumption during anomaly detection. Artificial intelligence is used for load balancing and routing to prevent quick energy draining. This work attempts to develop a scalable and adaptive anomaly-detection framework by combining artificial intelligence with energy-efficient clustering and security protocols, which will function effectively in heterogeneous wireless sensor network environments. Further, the suitability and reliability of the solution are proven by improved accuracy in detection, reduction of false positives, and enhancement of the network lifetime, thus paving the way for the securing of critical applications.