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Quantifying Catastrophic Forgetting in IoT Intrusion Detection Systems

arXiv:2603.00363v1 Announce Type: new Abstract: Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale connected systems. Intrusion Detection Systems (IDS) trained on static datasets often fail to generalize to unseen threats and suffer from catastrophic forgetting when updated with new attacks. Ensuring continual adaptability of IDS is therefore essential for maintaining robust IoT network defence. In this focused study, we formulate intrusion detection as a domain continual learning problem and propose a method-agnostic IDS framework that can integrate diverse continual learning strategies. We systematically benchmark five representative approaches across multiple domain-ordering sequences using a comprehensive multi-attack dataset comprising 48 domains. Results show that continual learning mitigates catastrophic forgetting while maintaining a balance between plasticity, stability, and efficiency, a

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Sourasekhar Banerjee, David Bergqvist, Salman Toor, Christian Rohner, Andreas Johnsson · · 1 min read · 16 views
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arXiv:2603.00363v1 Announce Type: new Abstract: Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale connected systems. Intrusion Detection Systems (IDS) trained on static datasets often fail to generalize to unseen threats and suffer from catastrophic forgetting when updated with new attacks. Ensuring continual adaptability of IDS is therefore essential for maintaining robust IoT network defence. In this focused study, we formulate intrusion detection as a domain continual learning problem and propose a method-agnostic IDS framework that can integrate diverse continual learning strategies. We systematically benchmark five representative approaches across multiple domain-ordering sequences using a comprehensive multi-attack dataset comprising 48 domains. Results show that continual learning mitigates catastrophic forgetting while maintaining a balance between plasticity, stability, and efficiency, a crucial aspect for resource-constrained IoT environments. Among the methods, Replay-based approaches achieve the best overall performance, while Synaptic Intelligence (SI) delivers near-zero forgetting with high training efficiency, demonstrating strong potential for stable and sustainable IDS deployment in dynamic IoT networks.

Executive Summary

This article addresses the critical issue of catastrophic forgetting in Intrusion Detection Systems (IDS) for IoT networks. The authors formulate IDS as a domain continual learning problem and propose a method-agnostic framework that integrates diverse continual learning strategies. The study systematically benchmarks five representative approaches on a comprehensive multi-attack dataset and demonstrates that continual learning mitigates catastrophic forgetting while maintaining a balance between plasticity, stability, and efficiency. The results show that Replay-based approaches achieve the best overall performance, while Synaptic Intelligence (SI) delivers near-zero forgetting with high training efficiency. This research has significant implications for the development of stable and sustainable IDS deployment in dynamic IoT networks.

Key Points

  • ▸ Catastrophic forgetting in IDS for IoT networks is a critical issue that can compromise the reliability and security of large-scale connected systems.
  • ▸ The authors propose a method-agnostic IDS framework that integrates diverse continual learning strategies to mitigate catastrophic forgetting.
  • ▸ The study benchmarks five representative approaches on a comprehensive multi-attack dataset and demonstrates the effectiveness of continual learning in IDS for IoT networks.

Merits

Strength in Methodological Approach

The authors' use of a method-agnostic framework and systematic benchmarking of multiple approaches provides a comprehensive evaluation of the effectiveness of continual learning in IDS for IoT networks.

Empirical Evidence

The study provides empirical evidence of the benefits of continual learning in mitigating catastrophic forgetting in IDS for IoT networks, which is essential for the development of stable and sustainable IDS deployment.

Demerits

Limited Scope

The study focuses on a specific type of IDS (RPL-based) and a specific type of dataset (multi-attack dataset), which may limit the generalizability of the findings to other types of IDS and datasets.

Lack of Theoretical Analysis

The study does not provide a thorough theoretical analysis of the proposed method-agnostic framework and its implications for the development of IDS for IoT networks.

Expert Commentary

The article presents a timely and relevant study on the critical issue of catastrophic forgetting in IDS for IoT networks. The authors' use of a method-agnostic framework and systematic benchmarking of multiple approaches provides a comprehensive evaluation of the effectiveness of continual learning in IDS for IoT networks. The study's findings have significant practical implications for the development of stable and sustainable IDS deployment in dynamic IoT networks. However, the study's limited scope and lack of theoretical analysis are notable limitations that should be addressed in future research. Overall, the study makes a significant contribution to the field of machine learning for IoT security and has the potential to inform policy decisions and practical applications.

Recommendations

  • ✓ Future research should focus on extending the study's findings to other types of IDS and datasets to increase the generalizability of the results.
  • ✓ The authors should provide a more thorough theoretical analysis of the proposed method-agnostic framework and its implications for the development of IDS for IoT networks.

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