Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei Taiwan.
PLoS One. 2024 Sep 11;19(9):e0308469. doi: 10.1371/journal.pone.0308469. eCollection 2024.
In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset and autoencoders for anomaly detection. The trained autoencoder is integrated with XGBoost and Random Forest, resulting in the models XGBoost-AE and Random Forest-AE. The study demonstrates that incorporating an anomaly detector into traditional models significantly enhances performance. The Random Forest-AE model achieved 100% accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), outperforming the methods proposed by Balasubramanian et al., Khan, Mezina et al., Smith et al., and Dener et al. When tested on unseen data, the Random Forest-AE model achieved an accuracy of 99.9892%, precision of 100%, recall of 99.9803%, F1 score of 99.9901%, and MCC of 99.8313%. This research highlights the effectiveness of the proposed model in maintaining high accuracy even with previously unseen data.
在这个数字化连接无处不在的时代，网络安全问题日益严重。技术的快速发展带来了一系列的网络威胁，包括复杂的零日攻击。本研究使用 CIC-MalMem-2022 数据集和自动编码器解决了现有入侵检测系统在识别零日攻击方面的挑战，用于异常检测。训练好的自动编码器与 XGBoost 和随机森林集成，形成了 XGBoost-AE 和随机森林-AE 两种模型。研究表明，将异常检测器集成到传统模型中可以显著提高性能。随机森林-AE 模型在准确率、精度、召回率、F1 得分和马修斯相关系数（MCC）方面均达到 100%，优于 Balasubramanian 等人、Khan、Mezina 等人、Smith 等人和 Dener 等人提出的方法。在对未见数据进行测试时，随机森林-AE 模型的准确率为 99.9892%，精度为 100%，召回率为 99.9803%，F1 得分为 99.9901%，MCC 为 99.8313%。这项研究强调了所提出模型在处理以前未见数据时保持高准确率的有效性。
