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《Acta Electronica Sinica》 2017-03
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A Lightweight Intrusion Detection Model Based on Autoencoder Network with Feature Reduction

GAO Ni;GAO Ling;HE Yi-yue;WANG Hai;School of Information Science and Technology,Northwest University;School of Information,Xi'an University of Finance and Economics;School of Economics and Management,Northwest University;  
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 high-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 rawdada from high-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 newoptimal high-dimensional representation of data in low-dimensional space,and the corresponding optimal low-dimensional representation of rawdata can be obtained. Furthermore,SVMclassification algorithm is employed to detect intrusion from the optimal low-dimensional data. The experimental results demonstrate that AN-SVMmodel 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-SVMmodel is a feasible and efficient lightweight intrusion detection model.
【Fund】: 国家自然科学基金(No.61373176);; 教育部人文社会科学研究青年项目(No.16XJC630001);; 陕西省自然基金(No.2015JQ7278)
【CateGory Index】: TP393.08;TP18
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