Trust Aware Water Strider Optimization-Based Clustering with Intrusion Detection in IoT Environments

The Internet of Things (IoT) has emerged as a transformative technology revolutionizing domains such as healthcare, smart cities, and e-governance. However, real-time IoT integration faces significant hurdles due to the limited energy resources and computational capabilities of IoT devices. Additionally, security vulnerabilities in IoT systems amplify threats to the integrity and reliability of smart applications, necessitating robust solutions. This study introduces a novel Trust-Aware Improved Water Strider Optimization-based Clustering with Intrusion Detection System (TAIWSOC-IDS) for enhancing IoT environments' security and efficiency. The proposed framework comprises two key stages: clustering and intrusion detection. In the clustering stage, the TAIWSOC technique identifies clusters and cluster heads (CHs) by optimizing a fitness function that considers residual energy, communication distance, and trust metrics, ensuring balanced energy consumption and secure communication. In the intrusion detection stage, a Chaos Game Optimization (CGO)-enhanced Multihead Attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) model is employed. The CGO algorithm optimally tunes the hyper parameters of the MHA-BiLSTM model, improving its ability to accurately classify normal and malicious activities. Experimental evaluation demonstrates that the TAIWSOC-IDS outperforms existing state-of-the-art methods in terms of energy efficiency, clustering reliability, and intrusion detection accuracy. The proposed system achieves enhanced detection rates while significantly reducing false positives, proving its efficacy in mitigating security threats. By addressing both energy efficiency and security concerns, TAIWSOC-IDS serves as a comprehensive framework for developing secure, sustainable, and efficient IoT applications across diverse real-time scenarios.