A QoS-aware hybrid adaptive routing attack detection and mitigation approach for secure IoT-LLN environment

Abstract In the era of digital technology, the Internet of Things (IoT) plays an imperative role by connecting smart devices over divergent domains. Low Power and Lossy Networks (LLNs) balance the energy efficiency among multiple devices that enable real-time insights for numerous IoT applications particularly from simple automation systems to complex surveillance. However, the IoT–LLN is highly susceptible to a variety of attacks due to memory consumption, constrained battery storage and increased control packet overhead in the routing environment. These attacks severely impact on system responsiveness, data integrity and Quality of Service (QoS). To address these challenges, this paper introduces a novel Hybrid Routing Attack Detection and Mitigation Technique (HRADMT) for securing the IoT–LLN environment. The proposed protocol empowers them to identify the different vulnerabilities by detecting and classifying attacks before they can compromise the system. Primarily, the proposed HRADMT utilizes feature selection methods to detect attacks based on the behavior of nodes and energy patterns. It further invokes the attack classification phase in order to detect and categorize the different attacks like rank, wormhole, and distributed denial of service attacks. The performance of the proposed HRADMT has been evaluated under two different environments (Sparse LLN and Dense LLN). The simulation outcomes prove that the proposed HRADMT protocol achieves 96.18% accuracy when compared with the existing detection techniques.