Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dependent on external variables such as the types of attacks, complexity, and network environment in the dataset. For example, BoT_IoT and Stratosphere IoT datasets both capture IoT-related attacks, but the deep neural network performed the best when tested using the BoT_IoT dataset while HELAD performed the best when tested using the Stratosphere IoT dataset. So although we found that a deep neural network solution had the highest average F1 scores on tested datasets, it is not always the best-performing one. We further discuss difficulties in using IDS from literature and project repositories, which complicated drawing definitive conclusions regarding IDS selection.

本研究通过实证比较最近的入侵检测系统，以客观比较它们，帮助用户根据需求选择最合适的解决方案。我们的结果表明，没有一个解决方案是最好的，而是依赖于外部变量，如攻击类型、复杂性和数据集的网络环境。因此，虽然我们发现深度神经网络在测试数据集上具有最高的平均F1分数，但它并不总是表现最好的。我们进一步讨论了来自文献和项目存储库的使用入侵检测系统的困难，这使得对于入侵检测系统选择的明确结论变得复杂。

期望与现实：实践中评估入侵检测系统