Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways

• URL: http://arxiv.org/abs/2511.18240v1
• Date: Sun, 23 Nov 2025 00:59:27 GMT
• Title: Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways
• Authors: Saeid Jamshidi, Foutse Khomh, Kawser Wazed Nafi, Amin Nikanjam, Samira Keivanpour, Omar Abdul-Wahab, Martine Bellaiche,
• Abstract summary: This paper proposes two novel IDS: DeepEdgeIDS and AutoDRL-IDS.<n>Both DRL-based IDS are validated through theoretical analysis and experimental evaluation on edge gateways.<n>Results demonstrate that AutoDRL-IDS achieves 94% detection accuracy using labeled data, while DeepEdgeIDS attains 98% accuracy and adaptability without labels.
• Score: 5.868388890362134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, an unsupervised 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% accuracy and adaptability without labels. Distinctly, this study introduces a carbon-aware, multi-objective reward function optimized for sustainable and real-time IDS operations in dynamic IoT networks.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Latent Diffusion for Internet of Things Attack Data Generation in Intrusion Detection [2.2758077237273837]
  Intrusion Detection Systems (IDSs) are a key component for protecting Internet of Things (IoT) environments.<n>In Machine Learning-based (ML-based) IDSs, performance is often degraded by the strong class imbalance between benign and attack traffic.<n>We propose the use of a Latent Diffusion Model (LDM) for attack data augmentation in IoT intrusion detection.
  arXiv  Detail & Related papers  (2026-01-23T18:55:07Z)
• Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD [0.2399911126932527]
  This paper presents a novel approach to network anomaly detection using hyperdimensional computing (HDC) techniques.<n>The proposed method leverages the efficiency of HDC in processing large-scale data to identify both known and unknown attack patterns.<n>The model achieved an accuracy of 91.55% on the KDDTrain+ subset, outperforming traditional approaches.
  arXiv  Detail & Related papers  (2025-03-04T22:19:26Z)
• A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks [1.1970409518725493]
  Internet of things (IoT) networks, boosted by 6G technology, are transforming various industries.<n>Their widespread adoption introduces significant security risks, particularly in detecting rare but potentially damaging cyber-attacks.<n>Traditional IDS often struggle with detecting rare attacks due to severe class imbalances in IoT data.
  arXiv  Detail & Related papers  (2025-02-09T21:13:11Z)
• Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning [64.20847540439318]
  Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks.<n>Some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network.<n>This paper explores the solution of combining GNNs with DRL to build a resilient network.
  arXiv  Detail & Related papers  (2025-01-19T15:22:17Z)
• A Survey for Deep Reinforcement Learning Based Network Intrusion Detection [3.493620624883548]
  This paper explores the potential and challenges of using deep reinforcement learning (DRL) in network intrusion detection. The performance of DRL models is analyzed, showing that while DRL holds promise, many recent technologies remain underexplored. The paper concludes with recommendations for enhancing DRL deployment and testing in real-world network scenarios.
  arXiv  Detail & Related papers  (2024-09-25T13:39:30Z)
• D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning [99.33607114541861]
  We propose a new benchmark for offline RL that focuses on realistic simulations of robotic manipulation and locomotion environments. Our proposed benchmark covers state-based and image-based domains, and supports both offline RL and online fine-tuning evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:27:00Z)
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey [0.23408308015481666]
  State-of-the-art DRL-based IDS methods have been classified into five categories including wireless sensor network (WSN), deep Q-network (DQN), healthcare, hybrid, and other techniques. The most crucial performance metrics, namely accuracy, recall, precision, false negative rate (FNR), false positive rate (FPR), and F-measure, are detailed.
  arXiv  Detail & Related papers  (2024-05-30T13:19:23Z)
• Enhancing IoT Security Against DDoS Attacks through Federated Learning [0.0]
  Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Traditional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning.
  arXiv  Detail & Related papers  (2024-03-16T16:45:28Z)
• ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning [42.87245000172943]
  offline deep reinforcement learning ( offline DRL) is frequently used to train models on pre-collected datasets. We propose ORL-AUDITOR, which is the first trajectory-level dataset auditing mechanism for offline DRL scenarios. Our experiments on multiple offline DRL models and tasks reveal the efficacy of ORL-AUDITOR, with auditing accuracy over 95% and false positive rates less than 2.88%.
  arXiv  Detail & Related papers  (2023-09-06T15:28:43Z)
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
  We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
  arXiv  Detail & Related papers  (2023-04-20T17:11:05Z)
• Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP [62.81300791178381]
  The bottleneck of distributed edge learning over wireless has shifted from computing to communication. Existing TCP-based data networking schemes for DEL are application-agnostic and fail to deliver adjustments according to application layer requirements. We develop a hybrid multipath TCP (MP TCP) by combining model-based and deep reinforcement learning (DRL) based MP TCP for DEL.
  arXiv  Detail & Related papers  (2022-11-03T09:08:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.