The intrusion detection system divides into two categories according to the detection technique: anomaly-based detection system and misuse-based detection system.
入侵检测按照检测技术分为两类：基于异常的入侵检测和基于误用的入侵检测。
This paper focus on Anomaly-based Network Intrusion Detection System (ANIDS), which use two methods to design and implement anomaly detection .
ANIDS是基于异常检测技术的入侵检测系统，它从两个方面来实现异常检测。
Use data mining methods to analyze the audit data and provide anomaly detection based on the generated normal patterns, this method can improve the performance of intrusion detection system.
利用数据挖掘技术对审计数据加以分析，总结出一些正常模式，用来进行异常检测，将有助于提高入侵检测系统的检测准确性和完备性。
The ID analysis methods have two ways: one is anomaly detection and the other is misuse detection. Nowadays, the most popular IDS is network intrusion detection system using misuse detection method.
入侵检测的分析技术主要分为滥用入侵检测和异常入侵检测，目前国内外流行的网络入侵检测系统大都是采用滥用入侵检测技术。
To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).
针对网络入侵的不确定性导致异常检测系统误报率较高的不足，提出一种基于Q-学习算法的异常检测模型（QLADM）。 该模型把Q-学习、行为意图跟踪和入侵预测结合起来，可获得未知入侵行为的检测和响应。
To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).
针对网络入侵的不确定性导致异常检测系统误报率较高的不足，提出一种基于Q-学习算法的异常检测模型（QLADM）。 该模型把Q-学习、行为意图跟踪和入侵预测结合起来，可获得未知入侵行为的检测和响应。
应用推荐