Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine - Jialin WANG; Jiqiang LIU; Di ZHAO; Yingdi WANG; Yingxiao XIANG; Tong CHEN; Endong TONG; Wenjia NIU (网络与信息安全学报)
Network intrusion detection system plays an important role in protecting network security.With the continuous development of science and technology,the current intrusion technology cannot cope with the modern complex and volatile network abnormal traffic,without taking into account the scalability,sustainability and training time of the detection technology.Aiming at these problems,a new deep learning method was proposed,which used unsupervised non-symmetric convolutional auto-encoder to learn the characteristics of the data.In addition,a new method based on the combination of non-symmetric convolutional auto-encoder and multi-class support vector machine was proposed.Experiments on the data set of KDD99 show that the method achieves good results,significantly reduces training time compared with other methods,and further improves the network intrusion detection technology.