基于对抗性自编码器的入侵检测算法

Intrusion Detection Algorithm Based on Adversarial Autocoder

针对当前无监督学习的入侵检测算法准确度低、误报率高以及有监督学习算法所需训练样本标记成本高的问题,提出一种基于对抗性自编码器的入侵检测算法.这是一种半监督学习算法,仅需要训练数据集中少量标记数据进行训练,并在训练数据集中支持未标记数据,从而提高性能.首先,自编码器通过提取重要特征作为潜在变量来降低输入数据的维数;其次,利用生成对抗网络使自编码器的潜在变量遵循任意分布以进行正则化;最后,利用标记数据的交叉熵损失来实现半监督学习的分类.实验结果表明:相较于其他算法,本文所提算法对少量标记的数据集检测具有一定的优势,在实现高准确度、低误报率的同时,降低对标记数据的需求.

Aiming at the problems of low accuracy and high false alarm rate for intrusion detection algorithm based on unsupervised learning,and high cost of training samples required by supervised algorithm,an intrusion detection algorithm based on adversarial autocoder has been proposed.This is a semi-supervised learning algorithm,which only needs a small amount of labeled data in the training data set for training,and supports unlabeled data in the training data set,so as to improve the performance.Firstly,the autocoder reduces the dimensionality of the input data by extracting important features as latent variables;secondly,it uses the generative adversarial network to make the latent variables of the autocoder follow an arbitrary distribution for regularization;and finally,it uses the cross entropy loss of labeled data to achieve the classification of semi-supervised learning.Experimental results show that,compared with other algorithms,the proposed algorithm has certain advantages in detecting a limited number of labeled samples,which can achieve high accuracy and low false alarm rate,while reducing the demand for labeled data.