FusionSec-IoT: A Federated Learning-Based Intrusion Detection System for Enhancing Security in IoT Networks

Internet of Things (IoT) has become one of the most significant technological advancements of the modern era, which has impacted multiple sectors in the way it provides communication between connected devices. However, this growth has led to security risks in the IoT devices especially when constructing resource-limited IoT networks that are easily attacked by hackers through methods like DDoS and data theft. Traditional IDS such as centralized IDS and single-view machine learning-based IDS are incapable of providing efficient solutions to these issues due to high communication cost, latency, and low attack detection rate for IDS. To address these challenges, this paper presents FusionSec-IoT, a decentralized IDS based on multi-view learning and federated learning for better detection performance and scalability in the IoT context. The results sows that the proposed technique performs better than the existing IDS methods with 08.3% accuracy as compared to classic IDS techniques centralized IDS (91.5%) and single-view federated learning (92.7%). The other performance metrics like shows a better performance as compared to traditional IDS methods. Thus, FusionSec-IoT detects a broad range of cyberattacks with the help of the employed complex machine learning algorithms such as Reinforcement Learning, Meta-Learning, and Hybrid Feature Selection using Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA), and ensures data privacy is maintained. Moreover, Edge Computing and Graph Neural Networks (GNNs) guarantee fast identification of multi-device coordinated attacks, for instance, botnets. The above-discussed proposed system enhances the traditional IDS approaches in terms of high detection accuracy, better privacy, and scalability, making the proposed system a reliable solution to secure the complex and large-scale IoT networks.