Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

Abstract

In the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy-efficient data transmission in such environments is crucial for mission-critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q-learning Protocol (dDC-ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real-time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end-to-end-delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).