摘要
网络入侵检测系统在防护网络安全中占据重要地位,随着科技不断发展,目前的入侵技术没有考虑到检测技术的可扩展性、可持续性以及训练时间长短,无法应对现代复杂多变的网络异常流量。针对这些问题,提出了一种新的深度学习方法,使用无监督的非对称卷积自编码器,对数据进行特征学习。另外,提出了一种新的基于非对称卷积自编码器和多类支持向量机相结合的方法。在KDD99数据集上进行了实验,实验结果表明,该方法取得了良好的结果,与其他方法相比显著减少了训练时间,进一步提高了网络入侵检测技术。
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 KDD99show that the method achieves good results,significantly reduces training time compared with other methods,and further improves the network intrusion detection technology.
作者
王佳林
刘吉强
赵迪
王盈地
相迎宵
陈彤
童恩栋
牛温佳
WANG Jialin;LIU Jiqiang;ZHAO Di;WANG Yingdi;XIANG Yingxiao;CHEN Tong;TONG Endong;NIU Wenjia(Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing 100044, China)
出处
《网络与信息安全学报》
2018年第11期58-68,共11页
Chinese Journal of Network and Information Security
基金
中央高校基础研究基金资助项目(No.2017RC016
No.2018JBZ103)
国家自然科学基金资助项目(No.61672092)
信息保障科技实验室基金资助项目(No.614200103011711)
北京优秀人才培养基金资助项目(No.BMK2017B02-2)
国家留学基金委资助项目(No.201807095014)~~
关键词
入侵检测技术
卷积自编码器
支持向量机
网络安全
intrusion detection technology
convolutional auto-encoder
support vector machine
network security
