基于自编码网络特征降维的轻量级入侵检测模型

基于支持向量机(SVM)的入侵检测方法受时间和空间复杂度约束,在高维特征空间计算时面临“维数灾害”的问题.为此,本文提出一种基于自编码网络的支持向量机入侵检测模型(AN-SVM).首先,该模型采用多层无监督的限制玻尔兹曼机(RBM)将高维、非线性的原始数据映射至低维空间,建立高维空间和低维空间的双向映射自编码网络结构,进而运用基于反向传播网络的自编码网络权值微调算法重构低维空间数据的最优高维表示,从而获得原始数据的相应最优低维表示;最后,采用SVM分类算法对所学习到的最优低维表示进行入侵识别.实验结果表明,AN-SVM模型降低了入侵检测模型中分类的训练时间和测试时间,并且分类效果优于传统算法,是一种可行且高效的轻量级入侵检测模型.

A Lightweight Intrusion Detection Model Based on Autoencoder Network with Feature Reduction

Owing to the constraints of time and space complexity,support vector machine (SVM) faced with the problem of ‘curse of dimensionality’ when computation happens in nigh-dimensional feature space.Therefore,an intrusion detection model of support vector machine based on autoencoder network (AN-SVM) is proposed.First,the multilayer unsupervised restricted boltzmann machine (RBM) in our model is employed in mapping the vector of raw dada from nigh-dimensional nonlinear space to low-dimensional space,and a mutual mapping autoencoder network of high-dimensional space and low-dimensional space is constructed.Then autoencoder network weights of fine-tuning algorithm based on back propagation network is employed to reconstruct the new optimal nigh-dimensional representation of data in low-dimensional space,and the corresponding optimal low-dimensional representation of raw data can be obtained.Furthermore,SVM classification algorithm is employed to detect intrusion from the optimal low-dimensional data.The experimental results demonstrate that AN-SVM model can effectively reduce the training time and testing time of classifier in the intrusion detection model and its classification performance outperforms those traditional methods.So,AN-SVM model is a feasible and efficient lightweight intrusion detection model.