Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review
Vasilis Ieropoulos, Eirini Anthi, Theodoros Spyridopoulos, Pete Burnap, Ioannis Mavromatis, Aftab Khan, Pietro Carnelli · Journal of Information Security and Applications · 2025
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.