Emanet Sura, Karatas Baydogmus Gozde, Demir Onder
Marmara University Istanbul, Istanbul, Turkey.
PeerJ Comput Sci. 2023 Sep 29;9:e1553. doi: 10.7717/peerj-cs.1553. eCollection 2023.
Intrusion detection systems (IDSs) analyze internet activities and traffic to detect potential attacks, thereby safeguarding computer systems. In this study, researchers focused on developing an advanced IDS that achieves high accuracy through the application of feature selection and ensemble learning methods. The utilization of the CIC-CSE-IDS2018 dataset for training and testing purposes adds relevance to the study. The study comprised two key stages, each contributing to its significance. In the first stage, the researchers reduced the dataset through strategic feature selection and carefully selected algorithms for ensemble learning. This process optimizes the IDS's performance by selecting the most informative features and leveraging the strengths of different classifiers. In the second stage, the ensemble learning approach was implemented, resulting in a powerful model that combines the benefits of multiple algorithms. The results of the study demonstrate its impact on improving attack detection and reducing detection time. By applying techniques such as Spearman's correlation analysis, recursive feature elimination (RFE), and chi-square test methods, the researchers identified key features that enhance the IDS's performance. Furthermore, the comparison of different classifiers showcased the effectiveness of models such as extra trees, decision trees, and logistic regression. These models not only achieved high accuracy rates but also considered the practical aspect of execution time. The study's overall significance lies in its contribution to advancing IDS capabilities and improving computer security. By adopting an ensemble learning approach and carefully selecting features and classifiers, the researchers created a model that outperforms individual classifier approaches. This model, with its high accuracy rate, further validates the effectiveness of ensemble learning in enhancing IDS performance. The findings of this study have the potential to drive future developments in intrusion detection systems and have a tangible impact on ensuring robust computer security in various domains.
入侵检测系统（IDS）分析互联网活动和流量以检测潜在攻击，从而保护计算机系统。在本研究中，研究人员专注于开发一种先进的IDS，通过应用特征选择和集成学习方法实现高精度。使用CIC - CSE - IDS2018数据集进行训练和测试增加了该研究的相关性。该研究包括两个关键阶段，每个阶段都有其重要意义。在第一阶段，研究人员通过战略特征选择减少数据集，并精心选择用于集成学习的算法。这个过程通过选择最具信息性的特征并利用不同分类器的优势来优化IDS的性能。在第二阶段，实施了集成学习方法，产生了一个强大的模型，该模型结合了多种算法的优点。研究结果证明了其对改进攻击检测和减少检测时间的影响。通过应用诸如斯皮尔曼相关性分析、递归特征消除（RFE）和卡方检验方法等技术，研究人员确定了增强IDS性能的关键特征。此外，不同分类器的比较展示了诸如极端随机树、决策树和逻辑回归等模型的有效性。这些模型不仅实现了高准确率，还考虑了执行时间的实际方面。该研究的总体意义在于其对提升IDS能力和改善计算机安全的贡献。通过采用集成学习方法并精心选择特征和分类器，研究人员创建了一个优于单个分类器方法的模型。这个具有高准确率的模型进一步验证了集成学习在增强IDS性能方面的有效性。本研究的结果有可能推动入侵检测系统的未来发展，并对确保各个领域强大的计算机安全产生切实影响。
