数据挖掘技术在网络安全检测中的应用价值

在网络运行中,经常出现由于恶意网络攻击行为、网络配置失误等引起的异常网络流量,这些偏离正常范围的异常流量会直接对整个网络服务质量造成影响,导致网络瘫痪,因此在网络运行时,进行安全检测并及时提供预警信息对保障网络安全正常运行十分重要。本文在介绍数据额挖掘技术的基础上,对利用挖掘领域中的隐马尔科夫模型建立基于异常检测的入侵检测系统进行了分析,并通过仿真实验验证了这一系统的可靠性,论述了数据挖掘技术在网络安全检测中的应用价值。

The Value of Data Mining Technology on Network Security Detection

In network, malicious network attacks, network configuration errors often occurred, which would cause the abnormal network traffic. This abnormal network traffic deviate from the normal range would directly affect the quality of service throughout the network, which would cause paralysis of the network. Therefore in network operation, safety testing and provide timely warning information on the protection of the normal operation of network security is very important. This paper introduced the amount of data mining techniques, and analyzed the intrusion detection systemuse using hidden Markov model in mining areas. And validated the system reliability by simulation and discussed value of the data mining technology in network security detection.