DLTIDS: A Dual-Layer Trust-Based Intrusion Detection System for Blackhole Attacks in Wireless Sensor Networks

The increasing prevalence of Blackhole attacks in Wireless Sensor Networks (WSNs) necessitates advanced and robust detection mechanisms. This paper presents the DLTIDS (Dual-Layer Trust-Based Intrusion Detection System), a sophisticated approach designed to counteract Blackhole attacks by leveraging a trust-based framework. DLTIDS integrates two layers of defense: the initial layer evaluates node behavior through direct trust metrics, incorporating packet forwarding ratios and communication reliability. The secondary layer enhances security by analyzing indirect trust metrics, which aggregate feedback from neighboring nodes to identify anomalous behavior patterns indicative of potential Blackhole activity. This dual-layer approach ensures a comprehensive assessment of node trustworthiness, effectively isolating and mitigating malicious entities. The system's efficacy is validated through extensive simulations, demonstrating significant improvements in detection accuracy and reduction of false positives compared to existing methods. Furthermore, DLTIDS maintains scalability and adaptability, making it suitable for diverse WSN environments. This research contributes to the enhancement of WSN security paradigms, providing a resilient solution to the pervasive threat of Blackhole attacks through an innovative trust-based detection mechanism.