论文标题
用于在线入侵检测系统的流程挖掘算法
Process Mining Algorithm for Online Intrusion Detection System
论文作者
论文摘要
在本文中，我们考虑过程挖掘在入侵检测中的应用。我们提出了一种新型工艺开采启发的算法，用于在入侵检测系统（IDS）中使用预处理数据。该算法旨在处理网络数据包数据，并且在在线模式下效果很好，用于在线入侵检测。为了测试我们的算法，我们使用了包含几个常见攻击的CSE-CIC-IDS2018数据集。使用该算法预处理数据包数据，然后将其馈入检测器。我们将使用算法（ML）模型（ML）模型作为分类器报告实验，以验证我们的算法是否按预期工作；我们还测试了异常检测方法的性能，并在现有的预处理工具CICFlowMeter上进行了报告，以进行比较。
In this paper, we consider the applications of process mining in intrusion detection. We propose a novel process mining inspired algorithm to be used to preprocess data in intrusion detection systems (IDS). The algorithm is designed to process the network packet data and it works well in online mode for online intrusion detection. To test our algorithm, we used the CSE-CIC-IDS2018 dataset which contains several common attacks. The packet data was preprocessed with this algorithm and then fed into the detectors. We report on the experiments using the algorithm with different machine learning (ML) models as classifiers to verify that our algorithm works as expected; we tested the performance on anomaly detection methods as well and reported on the existing preprocessing tool CICFlowMeter for the comparison of performance.
