Publication

Abstract : The Internet of Things (IoT) has revolutionized numerous aspects of our lives, offering many applications that enhance convenience and comfort. However, alongside its significant benefits, IoT introduces several research challenges, with security emerging as a primary concern. Given the sensitive nature of the information exchanged within IoT environments, ensuring robust security measures is imperative. One prominent threat in IoT environments is the potential for malicious attacks, which can exploit vulnerabilities and disrupt network operations. Among these threats, blackhole attacks pose a particularly concerning risk, as they involve malicious entities dropping all incoming packets, disrupting routing operations, and impeding communication. To mitigate the risks posed by blackhole attacks and enhance the security of IoT networks, a novel approach known as the K-means clustering-based Trust (KmeansT) evaluation mechanism has been proposed. This innovative method employs a multifaceted trust evaluation process, incorporating both direct observations and recommendations from other network entities. By leveraging the K-means clustering algorithm, the proposed mechanism enhances the effectiveness of trust evaluation, enabling a more accurate assessment of node reliability and integrity. One of the key strengths of the KmeansT approach lies in its ability to identify and mitigate blackhole attacks within the IoT environment effectively. Through rigorous mathematical modeling and simulation studies, the efficacy of the proposed mechanism in detecting and neutralizing blackhole threats is demonstrated. Simulation results are analyzed comprehensively, with performance metrics compared against existing models to assess the effectiveness of the KmeansT approach. By evaluating constraints such as end-to-end delay, packet delivery, and detection ratio, the superiority of the anticipated mechanism in safeguarding IoT networks against blackhole attacks is underscored.