随着网络技术的飞速发展,网络安全问题日益成为我们越来越担心的问题。在系统自带的防火墙之下,以及各大杀毒软件推出的防火墙技术,都还是难以确保网络的安全性。因此入侵检测系统变得日益受到人们观注。传统的基于规则的入侵检测,不仅系统资源占量大,而且面对复杂的网络系统和层出不穷的黑客攻击技术,有着明显的时间和空间上的局限性,因此传统的检测技术极易造成漏报和虚警。为了提高检测效率和检测准确率,本文提出了一种基于决策树分类算法的入侵检测系统。通过实验证明该入侵检测系具有较高的检测效率和检测准确率。 With the rapid development of network technology, the issue of network security is increasingly becoming a problem that we are increasingly worried about. In the system comes under the firewall, as well as the major anti-virus software introduced firewall technology, are still difficult to ensure network security. Therefore, intrusion detection system has become increasingly subject to people’s attention. Traditional rule-based intrusion detection not only has a large amount of system resources, but also has obvious time and space limitations in the face of complicated network systems and endless hacking techniques. Therefore, the traditional detection technology can easily lead to underreporting And false alarm. In order to improve the detection efficiency and detection accuracy, this paper presents an intrusion detection system based on the decision tree classification algorithm. The experiment proved that the intrusion detection system has higher detection efficiency and detection accuracy.