Blockchain-Enabled Lightweight Intrusion Detection System for Secure MANETs

In this study, we provide a novel method dubbed BL-IDS, which makes use of a decentralized Blockchain network to power a minimal intrusion detection system (IDS) for MANETs. The unique infrastructure-less nature of MANETs poses challenges in developing an effective and reliable IDS. Our proposed solution addresses these challenges by incorporating several key elements. First, we employ a hybrid authentication mechanism to authenticate network nodes, ensuring a secure environment. Authenticated nodes are then allowed to form clusters based on a similarity index (SI), facilitating efficient communication and routing within the network. To optimize routing, we introduce the wild horse optimization algorithm, which considers multiple metrics to select the most optimal route. To detect and mitigate intrusions, we propose the deep actor-critic learning (DAC) approach. This algorithm learns features from network packets through the Blockchain, which serves as a monitoring system for data transmission. If an intrusion is detected, the DAC algorithm updates the network with feedback, enabling prompt response and resolution. By combining these algorithms, our proposed approach achieves improved efficiency in various metrics, including PDR, throughput, time analysis, detection accuracy, and security level. The results demonstrate the effectiveness of our lightweight IDS driven by a decentralized Blockchain network in enhancing the overall performance and security of MANETs.