Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914581046099968 |
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| author | Jamshidi, Saeid Khomh, Foutse Nafi, Kawser Wazed Nikanjam, Amin Keivanpour, Samira Abdul-Wahab, Omar Bellaiche, Martine |
| author_facet | Jamshidi, Saeid Khomh, Foutse Nafi, Kawser Wazed Nikanjam, Amin Keivanpour, Samira Abdul-Wahab, Omar Bellaiche, Martine |
| contents | The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in mitigating Distributed Denial-of-Service (DDoS) attacks at the network edge. Traditional Intrusion Detection Systems (IDSs) face significant limitations, including poor adaptability to evolving and zero-day attacks, reliance on static signatures and labeled datasets, and inefficiency on resource-constrained edge gateways. Moreover, most existing DRL-based IDS studies overlook sustainability factors such as energy efficiency and carbon impact. To address these challenges, this paper proposes two novel Deep Reinforcement Learning (DRL)-based IDS: DeepEdgeIDS, a label-free Autoencoder-DRL hybrid, and AutoDRL-IDS, a supervised LSTM--DRL model. Both DRL-based IDS are validated through theoretical analysis and experimental evaluation on edge gateways. Results demonstrate that AutoDRL-IDS achieves 94% detection accuracy using labeled data, while DeepEdgeIDS attains 98% offline evaluation accuracy through label-free anomaly detection and online mitigation feedback. This study introduces a carbon-aware, multi-objective reward formulation that supports supervised reward optimization for AutoDRL-IDS and label-free online reward learning for DeepEdgeIDS, enabling sustainable real-time IDS operation in dynamic IoT networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways Jamshidi, Saeid Khomh, Foutse Nafi, Kawser Wazed Nikanjam, Amin Keivanpour, Samira Abdul-Wahab, Omar Bellaiche, Martine Cryptography and Security The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in mitigating Distributed Denial-of-Service (DDoS) attacks at the network edge. Traditional Intrusion Detection Systems (IDSs) face significant limitations, including poor adaptability to evolving and zero-day attacks, reliance on static signatures and labeled datasets, and inefficiency on resource-constrained edge gateways. Moreover, most existing DRL-based IDS studies overlook sustainability factors such as energy efficiency and carbon impact. To address these challenges, this paper proposes two novel Deep Reinforcement Learning (DRL)-based IDS: DeepEdgeIDS, a label-free Autoencoder-DRL hybrid, and AutoDRL-IDS, a supervised LSTM--DRL model. Both DRL-based IDS are validated through theoretical analysis and experimental evaluation on edge gateways. Results demonstrate that AutoDRL-IDS achieves 94% detection accuracy using labeled data, while DeepEdgeIDS attains 98% offline evaluation accuracy through label-free anomaly detection and online mitigation feedback. This study introduces a carbon-aware, multi-objective reward formulation that supports supervised reward optimization for AutoDRL-IDS and label-free online reward learning for DeepEdgeIDS, enabling sustainable real-time IDS operation in dynamic IoT networks. |
| title | Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2511.18240 |