Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review

The rapid growth of technology has increased interconnected large-scale systems, broadening the attack surface for malicious actors. Traditional security solutions often employ centralised management of components like firewalls and intrusion detection systems for consistent configuration. This centralisation introduces a ”single point of failure,” risking severe consequences if compromised. While redundancy can mitigate concerns in IT systems, it does not scale well for larger systems. Edge computing, which pushes computation closer to endpoint devices, has been explored to improve scalability. The research community has also explored distributing and decentralising cybersecurity operations, especially intrusion detection, using new machine learning methods that mix centralised and distributed approaches to scale effectively while preserving data privacy. However, challenges remain in implementing these methods in large-scale IoT systems due to resource constraints. This paper evaluates intrusion detection methods in large-scale, resource-limited IoT systems, exploring the benefits of low-powered devices for network security and discussing solutions to current implementation challenges. • Centralised IDS face limitations in IoT due to scarce resources and diverse data sources. • Federated Learning helps detect intrusions in IoT while preserving user privacy. • Techniques like robust aggregation address poisoning and rogue IoT devices.