基于随机森林和深度学习分类器的递归特征消除入侵检测系统

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI:10.1109/IBIGDELFT.2018.8625318

S. Ustebay, Zeynep Turgut, M. Aydin

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引用次数: 77

摘要

本文提出了一种入侵检测系统(IDS)来检测计算机网络中的恶意行为。该系统在CICIDS2017数据集上进行了研究，该数据集是在线可用的最大数据集。为了克服大数据带来的挑战，其目的是确定特征对数据集的影响，并找到能够以最有意义的方式区分数据的最有效特征。因此，通过随机森林进行递归特征消除，并计算特征的重要值。利用得到的特征，采用深度多层感知器(Deep Multilayer Perceptron, DMLP)结构对入侵进行检测，准确率达到91%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier

In this study, an intrusion detection system (IDS) has been proposed to detect malicious in computer networks. The proposed system is studied on the CICIDS2017 dataset, which is the biggest dataset available online. In order to overcome the challenges big data created, it is aimed to determine the effects of the features on the data set and to find the most effective features that can differentiate the data in the most meaningful way. Therefore, recursive feature elimination is performed via random forest and the importance value of the features are calculated. Intrusions are detected with the accuracy of 91% by Deep Multilayer Perceptron (DMLP) structure using the obtained features.

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2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)

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