A Comprehensive Security Framework for Wireless Sensor Networks using SHA256 and CNNs

Abstract

Wireless Sensor Networks (WSNs) serve as vital infrastructure across various domains; yet face escalating cyber threats that challenge their security and integrity. Current security approaches exhibit limitations, such as inadequate granularity in addressing node and cluster head vulnerabilities, and a lack of cohesive prevention-detection strategies. In response, this research proposes a novel two-phase security method tailored for WSNs. The first phase employs Convolutional Neural Networks (CNNs) optimized with the Emperor Penguin algorithm for precise intrusion detection at the node level. Enhanced data security is ensured through integration of the SHA256 hashing algorithm, bolstering both prevention and detection strategies. Subsequently, the second phase extends this approach to cluster heads, forming a cohesive security framework informed by the first phase's output. This comprehensive methodology not only addresses current challenges but also represents a significant leap forward in WSN security, promising robust protection against evolving threats. The obtained results indicate that the proposed method not only enhance the security of both nodes and clusterheads, but also integrating SHA256 can improve preventation without harming the detection phase.