基于机器学习的混合级联网络入侵检测方法

Hybrid cascade network intrusion detection method based on machine learning

入侵检测系统可以识别异常模式和流量,并对系统中未授权活动进行监测、检测和响应,是网络安全领域的重要研究方向。文章对当前网络环境下入侵数据维度高的问题进行分析,总结了常见的特征选择降维方法,并根据不同的入侵检测方法在实际应用中的优缺点,提出混合级联的检测思路,在提高检测准确率的同时兼顾效率。

Intrusion detection system can identify abnormal patterns and traffic, and monitor, detect and respond to unauthorized activities in the system. It is an important research direction in the field of network security. This paper analyzes the problem of high dimensionality of intrusion data in the current network environment, summarizes the common feature selection dimensionality reduction methods, and puts forward a hybrid cascade detection idea according to the advantages and disadvantages of different intrusion detection methods in practical application, which takes into account the efficiency while improving the detection accuracy.