SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset
SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset
复制标题
UNSW-NB15 数据集基于 SVM 的网络入侵检测
DOI：
10.1109/asicon47005.2019.8983598
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Hai
中科院分区：
文献类型：
--
作者：
Dishan Jing;Hai
随着互联网安全问题的日益增多，网络入侵检测系统成为物联网环境中不可或缺的一部分。过去，入侵检测的研究大多是在KDDCUP99数据集上进行的。然而，与UNSW-NB15数据集相比，KDDCUP99数据集在评估NIDS时缺乏一些典型的示例。本文提出了一种新的尺度方法--支持向量机，用于二分类和多分类实验。从准确率、检测率和误检率三个方面评价了该方法的性能。实验结果表明，与其他方法相比，本文提出的支持向量机方法具有一定的优越性。该方法对二值分类的正确率为85.99%，而对期望最大化(EM)聚类的正确率为78.47%。对于多分类问题，本文提出的支持向量机方法的测试正确率为75.77%，比朴素贝叶斯(NB)方法提高了6.17个百分点。
Due to the growth of internet security issues, Network Intrusion Detection System (NIDS) becomes an integral part of the IoT environment. In the past, most research on intrusion detection was experimented with the KDDCUP99 dataset. However, the KDDCUP99 dataset lacks some typical examples when evaluating NIDS compared with the UNSW-NB15 dataset. In this paper, we propose Support Vector Machine (SVM) with a new scaling method for binary-classification and multi-classification experiments. The performance of our method is evaluated through accuracy, detection rate and false positive rate. Compared with other methods, the superiority of the proposed SVM method is shown by the experimental results. The accuracy of the proposed method reaches 85.99% for binary-classification, compared to 78.47% by Expectation-Maximization (EM) clustering. For multi-classification, the proposed SVM method can achieve the testing accuracy of 75.77%, which is 6.17% higher than that of Naïve Bayes (NB).