Intrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are available. Machine learning ensemble methods have a well-proven track record when it comes to learning. Using ensemble methods of machine learning, this paper proposes an innovative intrusion detection system. To improve classification accuracy and eliminate false positives, features from the CICIDS-2017 dataset were chosen. This paper proposes an intrusion detection system using machine learning algorithms such as decision trees, random forests, and SVM (IDS). After training these models, an ensemble technique voting classifier was added and achieved an accuracy of 96.25%. Furthermore, the proposed model also incorporates the XAI algorithm LIME for better explainability and understanding of the black-box approach to reliable intrusion detection. Our experimental results confirmed that XAI LIME is more explanation-friendly and more responsive.

中文翻译：

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入侵检测系统的可解释人工智能

入侵检测系统广泛用于网络安全领域，以防止和减轻威胁。入侵检测系统 (IDS) 有助于将威胁和漏洞排除在计算机网络之外。为了开发有效的入侵检测系统，可以使用一系列机器学习方法。机器学习集成方法在学习方面具有良好的记录。使用机器学习的集成方法，本文提出了一种创新的入侵检测系统。为了提高分类准确性并消除误报，我们选择了 CICIDS-2017 数据集中的特征。本文提出了一种使用决策树、随机森林和 SVM (IDS) 等机器学习算法的入侵检测系统。训练完这些模型后，添加了一个集成技术投票分类器，准确率达到了 96.25%。此外，所提出的模型还结合了 XAI 算法 LIME，以更好地解释和理解可靠入侵检测的黑盒方法。我们的实验结果证实，XAI LIME 更易于解释且响应更快。