《计算机应用研究》|Application Research of Computers

基于ADBN的入侵检测方法

Intrusion detection method based on ADBN

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作者 江泽涛,周谭盛子
机构 桂林电子科技大学 广西图像图形智能处理重点实验室,广西 桂林 541004
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文章编号 1001-3695(2020)09-047-2797-05
DOI 10.19734/j.issn.1001-3695.2019.03.0149
摘要 当下大多数入侵检测算法无法在入侵检测率和误报率之间取得较好的平衡,为了有效避免此类问题,提出了一种基于非对称深度信念网络的入侵检测方法。该方法首先通过训练深度信念网络初始化ADBN(asymmetric deep belief network)模型中编码器部分的参数,利用正态分布初始化解码器部分的参数。然后通过计算重构误差来调优ADBN模型的参数,使模型能获取原始数据的最优低维表征。最后以编码器得到的数据作为分类器的输入数据并对其进行检测,采用ADBN模型可以提取出更有利于分类的特征且能够在模型初始化阶段节省更多的测试时间。实验结果表明,该方法可以达到更好的检测性能,对小类别样本也达到了较好的检测准确率。
关键词 入侵检测; 特征提取; 非对称深度信念网络; 编码器; 解码器
基金项目 国家自然科学基金资助项目(61762066,61876049)
广西图像图形智能处理重点实验项目(GIIP201701)
本文URL http://www.arocmag.com/article/01-2020-09-047.html
英文标题 Intrusion detection method based on ADBN
作者英文名 Jiang Zetao, Zhou Tanshengzi
机构英文名 Key Laboratory of Image & Graphic Intelligent Processing in Guangxi,Guilin University of Electronic Technology,Guilin Guangxi 541004,China
英文摘要 At present, most intrusion detection algorithm cannot achieve a good balance between intrusion detection rate and false positive rate, in order to effectively avoid such problems, this paper proposed an intrusion detection method based on ADBN. The method first initialized the parameters of the encoder part in the ADBN model by training the deep belief network, and initialized the parameters of the decoder part by using the normal distribution. Then it tuned the parameters of the asymmetric deep belief network model by calculating the reconstruction error, so that the model can obtain the optimal low-dimensional representation of the original data. Finally, it used the data obtained by the encoder as input data of the classifier and detected. The ADBN model can extract features that are more conducive to classification and save more test time in the model initialization phase. The experimental results show that the method can achieve better detection performance and achieve better detection accuracy for small categories of samples.
英文关键词 intrusion detection; feature extraction; asymmetric deep belief network; encoder; decoder
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收稿日期 2019/3/27
修回日期 2019/5/21
页码 2797-2801
中图分类号 TP309
文献标志码 A