摘要
机器学习应用于入侵检测的领域已经有很长一段时间。采用支持向量机的入侵检测手段有助于应对日益更新的攻击方法。从数据来源和分析的角度,可以将入侵检测模型分为基于工业流量,工业过程,用户行为,恶意文件四大类。在实验中,使用Matlab作为接口来完成与双容水箱系统的通信,通过区域之间状态的推导,就能准确地判断出遭受到攻击的区域。
Machine learning has been applied to intrusion detection for a long time.The intrusion detection method based on support vector machine is helpful to deal with the increasingly updated attack methods.From the perspective of data source and analysis,intrusion detection model can be divided into four categories:industrial traffic,industrial process,user behavior and malicious files.In the experiment,Matlab is used as the interface to complete the communication with the dual tank system.Through the derivation of the state between the areas,the area that is attacked can be accurately determined.
作者
肖铮
Xiao Zheng(Sichuan Technology&Business College,Chengdu City,Sichuan Province 611830)
出处
《黄河科技学院学报》
2020年第8期67-69,共3页
Journal of Huanghe S&T College
基金
教育部科技发展中心产学研创新基金(2018A03007)
四川省高等教育人才培养质量和教学改革项目(JG2018-1168)
2019年中国轻工业联合会教育工作分会立项课题(QGJY2019020)
四川工商职业技术学院校级课题(20190016)。
关键词
机器学习
支持向量机
异常检测
machine learning
support vector machine
anomaly detection
