基于机器学习的云基础设施网络防御：用于智能入侵检测与自动威胁缓解的自适应深度Q网络架构
Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
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中文总结 AI 辅助
该研究针对云环境复杂网络攻击问题，提出自适应DQN网络防御框架，经CICIDS2017等数据集验证，其入侵检测与威胁缓解性能优于多种传统机器学习模型，展现出强化学习在云自主网络安全中的应用潜力。
中文摘要 AI 辅助
随着云环境中网络攻击的复杂性不断提升，亟需能够支持实时检测与自主响应的自适应安全解决方案。本文提出一种基于强化学习的动态网络防御框架，部署深度Q网络（Deep Q-Network，DQN）以训练有效防御策略应对不断演变的网络攻击。研究采用CICIDS2017数据集构建模型，使用UNSW-NB15数据集进行外部验证，涉及数据预处理、特征工程与自适应策略学习环节。将所提DQN与决策树、支持向量机、随机森林、XGBoost及多层感知机模型进行对比，该DQN模型的准确率达99.72%、精确率99.68%、召回率99.65%、F1值99.66%、ROC-AUC为0.999，误报率0.31%、漏报率0.35%、检测延迟15ms；该框架的攻击缓解率达99.54%，展现出强大的自适应与实时防御能力，验证了强化学习作为现代云环境自主网络安全领域强大且可扩展方法的潜力。
英文摘要
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.
发表机构
- Wilmington University（威尔明顿大学）
- Washington University of Science and Technology（华盛顿科技大学）
- South Dakota School of Mines & Technology（南达科他矿业理工学院）
- South Dakota State University（南达科他州立大学）
- St. Francis College（圣弗朗西斯学院）
- Stanton University（斯坦顿大学）
机构由 AI 辅助整理，请以论文原文为准。
