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A Survey of Random Forest Based Methods for Intrusion Detection Systems

机译：基于随机森林的入侵检测方法研究

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摘要

Over the past decades, researchers have been proposing different Intrusion Detection approaches to deal with the increasing number and complexity of threats for computer systems. In this context, Random Forest models have been providing a notable performance on their applications in the realm of the behaviour-based Intrusion Detection Systems. Specificities of the Random Forest model are used to provide classification, feature selection, and proximity metrics. This work provides a comprehensive review of the general basic concepts related to Intrusion Detection Systems, including taxonomies, attacks, data collection, modelling, evaluation metrics, and commonly used methods. It also provides a survey of Random Forest based methods applied in this context, considering the particularities involved in these models. Finally, some open questions and challenges are posed combined with possible directions to deal with them, which may guide future works on the area.
机译：在过去的几十年中，研究人员一直在提出不同的入侵检测方法，以应对计算机系统不断增长的威胁数量和复杂性。在这种情况下，随机森林模型在基于行为的入侵检测系统领域中的应用上一直提供出色的性能。随机森林模型的特异性用于提供分类，特征选择和接近度度量。这项工作对与入侵检测系统有关的一般基本概念进行了全面的回顾，包括分类法，攻击，数据收集，建模，评估指标和常用方法。考虑到这些模型所涉及的特殊性，它还提供了在此背景下基于随机森林的方法的调查。最后，提出了一些悬而未决的问题和挑战，并结合可能的解决方法，以指导该领域的未来工作。

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