Fog-Assisted Anomaly Detection in Healthcare IoT Networks using Lightweight Blockchain and Collaborative Intrusion Detection Systems
Keywords:
Internet Of Things (IoT), Internet Of Medical Things (IoMT), Fog Computing, Blockchain Approach, Intrusion Detection System (IDS), Lion Salp Swarm Optimization Algorithm, Metaheuristic Algorithm and Healthcare.Abstract
The Internet of Things (IoT) has its footprint in each and every industry all over the world. Most industries are using IoT predominantly for its eminent performance, and the Internet of Medical Things (IoMT) is specifically used in Medical industries for providing effective and timely healthcare systems. Each and every data is counted as a precious thing in the medical field because a single alteration or modification in a patient’s data will lead to wrong treatment and may cause serious issues in the patient’s life. In the proposed work fog, fog-based anomaly detection is incorporated by using a lightweight blockchain approach. The raw data in a network is initially stored in local blocks, and after performing data aggregation and filtration, the selective blocks are transferred to the global block. These are further used for analysis purposes. The lightweight blockchain reduces the latency and improves efficiency. These blocks can be easily corrupted by malicious users or attackers. So, to enhance the security of the system, we have implemented a collaborative intrusion detection system based on the Lion Salp swarm optimization algorithm, which is a branch of the Metaheuristic algorithm. The proposed system has achieved 99.7% accuracy and 99.2% precision. This shows that the proposed work outperformed all other existing algorithms.
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