Multi-layer DDoS attacks detection with mitigation in IoT-enabled sensor network

Abstract The Internet of Things enabled Wireless Sensor Networks (IoT-enabled WSNs) are highly susceptible to a wide range of Distributed Denial of Service (DDoS) attacks. While several methods have been developed to identify and mitigate these attacks, creating a unified strategy to counter multi-layer DDoS attacks remains a challenge due to the diverse nature of threats presented at different layers of IoT communication networks. To address this, a new protocol called Multi-Layer DDoS threat Detection and Mitigation (MLDDM) has been proposed. MLDDM's core capability lies in its audit trail analysis of trusted networks. By continuously calculating trust parameters across various IoT layers, the trust-based network tree analysis method evaluates each sensor node within the network. This analysis is then used to detect and prevent DDoS attacks. The MLDDM is a comprehensive approach designed to detect and mitigate DDoS attacks across the application, transport, internet, and edge layers. The proposed multi-layer trust-based network tree method analyzes each node to determine whether it is a potential attacker, identifying DDoS threats and their root causes. Once a DDoS attack is detected, MLDDM disconnects the affected nodes from the IoT-enabled WSNs until the next scheduled check. Simulation results demonstrate that the MLDDM protocol outperforms existing solutions in terms of throughput, Packet Delivery Rate (PDR), energy efficiency, communication latency, and overhead.