Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems
Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems
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DOI：
10.1109/icmla55696.2022.00211
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Adam Lehavi;S. Kim
中科院分区：
文献类型：
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作者：
Adam Lehavi;S. Kim
在网络安全领域，入侵检测系统(入侵检测系统)根据收集的计算机和网络数据检测和预防攻击。在最近的研究中，已经使用机器学习(ML)和深度学习(DL)方法来构建入侵检测模型，例如随机森林(RF)和深度神经网络(DNN)。特征选择(FS)可用于构建更快、更易解释和更准确的模型。我们研究了三种不同的FS技术：RF信息增益(RF-IG)、使用BAT算法的相关特征选择(CFS-BA)和使用Aquila优化器的CFS(CFS-AO)。我们的结果表明，CFS-BA是FS方法中最有效的，建立最佳RF-IG模型的时间占最佳RF-IG模型的55%，同时达到99.99%的精度。这加强了先前对CFS-BA准确性的证明，同时建立了子集大小、CFS分数和最终结果中的RF-IG分数之间的关系。
In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature selection (FS) can be used to construct faster, more interpretable, and more accurate models. We look at three different FS techniques; RF information gain (RF-IG), correlation feature selection using the Bat Algorithm (CFS-BA), and CFS using the Aquila Optimizer (CFS-AO). Our results show CFS-BA to be the most efficient of the FS methods, building in 55% of the time of the best RF-IG model while achieving 99.99% of its accuracy. This reinforces prior contributions attesting to CFS-BA’s accuracy while building upon the relationship between subset size, CFS score, and RF-IG score in final results.