Table of parameters of the PVDM.

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.

随着网络安全风险日益高发，亟需制定针对性策略以实现高效的风险应对。本研究提出一种检测模型，其采用定制化方法论，整合了基于SHAP值（SHAP Values）的特征选择方法、名为PV-DM的浅层学习算法，以及XGBOOST等机器学习分类器。本研究依托NSL-KDD与UNSW-NB15数据集验证了所提方法论的有效性。在NSL-KDD数据集上，本研究方法展现出优异性能：准确率达98.92%、精确率98.92%、召回率95.44%、F1值96.77%。值得注意的是，该性能仅通过4个特征即可实现，印证了本方法的高效性。在UNSW-NB15数据集上，所提方法论仅使用6个特征，便实现了82.86%的准确率、84.07%的精确率、77.70%的召回率与80.20%的F1值。本研究结果充分证明，相较于传统深度学习模型，所提模型在各项性能指标上均实现了性能提升。