Heuristic Approach for Detecting and Neutralizing Black Hole Attacks in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, surveillance, and healthcare. However, the security of these networks remains a significant concern, particularly in the face of emerging threats like Black Hole Attacks. In Wireless Sensor Networks (WSNs), ensuring secure and reliable communication is significant for maintaining the integrity of transmitted data. Among various threats, Black Hole Attacks pose a severe risk, as they involve a malicious node selectively dropping packets, leading to substantial data loss and network disruption. This article presents a novel heuristic approach that leverages Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for the detection and neutralization of Black Hole Attacks in WSNs. By simulating various attack scenarios and leveraging the strengths of both algorithms, this research assesses their effectiveness in identifying the presence of Black Hole Attacks and subsequently devising countermeasures. Our experimental results demonstrate the effectiveness of this heuristic approach in terms of enhancing network security and maintaining optimal network performance. The study evaluates key performance indicators such as detection accuracy, computational complexity, and energy efficiency, aiming to provide insights into the most suitable heuristic approach for securing WSNs against Black Hole Attacks. This research contributes to the ongoing efforts to secure WSNs against malicious attacks, thereby facilitating the reliable and secure operation of wireless sensor applications. The results shed light on the comparative efficacy of GA and PSO, offering valuable guidance for designing robust security mechanisms in WSNs.