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.
