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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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【Secondary Citations】
Chinese Journal Full-text Database 10 Hits
1 WEI Qin-zhi (Beijing Force Control-Huacon Technology Co.,Ltd.,Beijing 100193,China);Industrial Network Control System Security and Management[J];测控技术;2013-02
2 Xia Chun-ming Liu Tao Wang Hua-zhong Wu Qing(School of Mechanical Engineering , East China University of Science and Technology Shanghai 200237);Industrial Control System Security Analysis[J];信息安全与技术;2013-02
3 PENG Yong1,2,JIANG Changqing1,XIE Feng1,DAI Zhonghua1, XIONG Qi1,GAO Yang1(1.China Information Technology Security Evaluation Center, Beijing 100085,China; 2.Information Security Center,Beijing University of Posts and Telecommunications,Beijing 100876,China);Industrial control system cybersecurity research[J];清华大学学报(自然科学版);2012-10
4 Zhang Yungui1,2 Zhao Hua2 Wang Lina2(1 School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China)(2 State Key Laboratory of Hybrid Process Industry Automation System and Equipment Technology,Automation Research and Design Institute of Metallurgical Industry,Beijing 100071,China);A non-parametric CUSUM intrusion detection method based on industrial control model[J];东南大学学报(自然科学版);2012-S1
5 WU Jun,LU Ming-yu,LIU Chuang(School of Information Science &Technology,Dalian Maritime University,Dalian,Liaoning 116026,China);SVM-Feedback Scheme Within Hybrid Learning Framework for Image Retrieval[J];电子学报;2010-09
6 LI Lin ZHANG Xiao-long (Wuhan University of Science & Technology,Wuhan 430081);Optimization of SVM with RBF Kernel[J];计算机工程与应用;2006-29
7 SUN Da-lin,JIANG Da-ming ( School of Electronics and Information Engineering, Beijing jiao tong university, Beijing 100044, China);Modbus/Tcp protocol safety and its application in industrial monitoring and control system[J];中国安全生产科学技术;2006-02
8 CHEN Guo-chu, YU Jin-shou (Research Institute of Automation, East China University of Science and Technology, Shanghai200237, China);Particle Swarm Optimization Algorithm[J];信息与控制;2005-03
9 LI Kun-lun 1, 2 , ZHAO Jun-zhong 1, HUANG Hou-kuan 1, TIAN Sheng-feng 1 (1. School of Computer & Information Technology, Beijing Jiaotong University, Beijing 100044, China;2. School of Mathematics & Computer Science, Hebei University, Baoding 071002, China);AN INTRUSION DETECTION METHOD BASED ON SVM[J];信息与控制;2003-06
10 ZHANG Xuegong (Dept.of Automation,Tsinghua University,Beijing 100084) (State Key Laboratory of Intelligent Technology and Systems of China);INTRODUCTION TO STATISTICAL LEARNING THEORY AND SUPPORT VECTOR MACHINES[J];自动化学报;2000-01
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