An ANN Based Sequential Detection Method for Balancing Performance Indicators of IDS
An ANN Based Sequential Detection Method for Balancing Performance Indicators of IDS
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一种基于ANN的平衡IDS性能指标的顺序检测方法
DOI：
10.1109/candar.2019.00039
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Kouichi Sakurai
中科院分区：
文献类型：
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作者：
Hao Zhao;Yaokai Feng;Hiroshi Koide;Kouichi Sakurai
近年来，网络攻击的数量迅速增加，网络安全已经成为一个重要的问题。入侵检测系统（IDS）作为防御网络威胁的一个重要组成部分被引入，机器学习算法被广泛应用于入侵检测系统中以提高检测性能。存在诸如假阳性率、假阴性率等的若干评价指标，问题是这些指标常常彼此相关。例如，当我们试图降低误报率时，误报率往往会增加，反之亦然。在这项研究中，我们提出了一个基于人工神经网络的顺序分类方法，以减轻这个问题。具体来说，我们试图训练ANN具有较低的假阳性率，尽管这可能导致较高的假阴性率。然后，将报告的否定实例发送到下一个ANN以进行进一步调查，其中在前一个ANN处报告的假否定实例可以被正确分类。这样，最终的漏报率也可以大大提高。实验结果表明，该方法在保证误报率的同时，具有较低的漏报率和较高的检测准确率。此外，在这项研究中，我们的建议的最佳数量的人工神经网络也进行了调查和讨论。
In recent years, the number of cyber attacks has been increasing rapidly and network security has become an important issue. As a vital component of defense against network threats, intrusion detection system (IDS) was introduced and machine learning algorithms have been widely used in such systems for high detection performance. There are several evaluation indices such as false positive rate, false negative rate, and so on. A problem is that these indices are often related to each other. For example, while we try to decrease the false positive rate, the false negative rate often tends to increase, and vice versa. In this study, we proposed an ANN based sequential classifier method to mitigate this problem. Specifically, we try to train ANN to have a low false positive rate, despite which may lead to high false negative rate. Then, the reported negative instances are sent to the next ANN to make a further investigation, where the false negative instances reported at the previous ANN may be classified correctly. In this way, the final false negative rate can also be improved greatly. The results of the experiment shows that the proposed method can bring lower false negative rate and higher accuracy of detection while making the false positive rate at an acceptable level. Moreover, the optimum number of ANNs for our proposal is also investigated and discussed in this study.
减少入侵检测系统误报的顺序分类器组合方法
DOI：
10.1587/transinf.2018ntp0019
发表时间：
2019
期刊：
IEICE Trans. Inf. Syst.
影响因子：
--
作者：
Sornxayya Phetlasy;S. Ohzahata;Celimuge Wu;Toshihito Kato
通讯作者：
Toshihito Kato