Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

• URL: http://arxiv.org/abs/2608.12190v1
• Date: Wed, 12 Aug 2026 15:46:17 GMT
• Title: Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
• Abstract summary: We propose a reinforcement learning-based dynamic cyber defense framework.<n>We deploy a Deep Q-Network (DQN) to train effective defensive strategies.<n>The framework achieved 99.54% attack mitigation rate, demonstrating strong adaptive and real-time defensive capabilities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework. We deploy a Deep Q-Network (DQN) to train effective defensive strategies to counteract the evolving cyberattacks. We leverage the CICIDS2017 dataset for model creation and the UNSW-NB15 dataset for external validation, involving preprocessing of data, feature engineering, and adaptive policy learning. We compare the proposed DQN with decision tree, support vector machine, random forest, XGBoost, and multilayer perceptron models. The proposed DQN achieves an accuracy of 99.72%, a precision of 99.68%, a recall of 99.65%, an F1-score of 99.66%, and an ROC-AUC of 0.999, while the false positive rate is 0.31%, the false negative rate is 0.35%, and the detection latency is 15 ms. The framework achieved 99.54% attack mitigation rate, demonstrating strong adaptive and real-time defensive capabilities. These results demonstrate the potential of reinforcement learning as a powerful and scalable approach for autonomous cybersecurity in modern cloud environments.

Related papers

• Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks [0.5098144539782359]
  This paper proposes QIRL, a Quantum-Inspired Reinforcement Learning framework for next-generation autonomous cyber defense.<n>QIRL combines amplitude-phase quantum state encoding, rotation-gate-based exploration, and quantum interference reward augmentation within a cost-sensitive Markov Decision Process formulation.<n>It attains ultra-low inference latencies of 32.5 and 45.7 microseconds per sample, corresponding to 67.77 times and 51.77 times speedups over ensemble baselines.
  arXiv  Detail & Related papers  (2026-06-05T19:33:15Z)
• NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs [2.068436123941345]
  We propose NERO-Net, a neuroevolutionary approach to design convolutional neural networks better equipped to resist adversarial attacks.<n>Our search strategy isolates architectural influence on robustness by avoiding adversarial training during the evolutionary loop.
  arXiv  Detail & Related papers  (2026-03-26T14:51:30Z)
• AI-Enhanced Intelligent NIDS Framework: Leveraging Metaheuristic Optimization for Robust Attack Detection and Prevention [1.4968127458030251]
  This research presents an AI-driven real-time intrusion detection framework designed to enhance network security.<n>The proposed system achieved 98.95 percent accuracy with Decision Tree, 98.47 percent with K-Nearest Neighbors, and 88.84 percent with Logistic Regression.
  arXiv  Detail & Related papers  (2025-08-31T15:20:11Z)
• AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
  AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
  arXiv  Detail & Related papers  (2025-04-29T17:36:05Z)
• Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP [51.04452017089568]
  Class-wise Backdoor Prompt Tuning (CBPT) is an efficient and effective defense mechanism that operates on text prompts to indirectly purify CLIP.<n>CBPT significantly mitigates backdoor threats while preserving model utility.
  arXiv  Detail & Related papers  (2025-02-26T16:25:15Z)
• Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms [11.657154571216234]
  Cloud computing environments are increasingly vulnerable to security threats.<n>Traditional security mechanisms, based on rule matching and feature recognition, struggle to adapt to evolving attack strategies.<n>This paper proposes an adaptive security protection framework leveraging deep learning to construct a multi-layered defense architecture.
  arXiv  Detail & Related papers  (2025-02-25T03:06:40Z)
• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks [22.618325281125916]
  3D point clouds play pivotal roles in various safety-critical applications, such as autonomous driving. We perform the first security analysis of state-of-the-art defenses and design adaptive evaluations on them. Our 100% adaptive attack success rates show that current countermeasures are still vulnerable.
  arXiv  Detail & Related papers  (2020-11-24T06:46:38Z)

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.