An intelligent framework to detect network intrusion

Along with the development of Internet applications, many kinds of network security issues become highlights. Customer confidentiality should be its highest priority for every Internet company. The network intrusion detection system as one of the key technology while auditing safely, is the important component of network safe protection. In this paper, we present an intelligent framework to detect network intrusions. We design two intrusion detection engines in the framework. One is the rule-based that depends on the programmed rules to detect intrusions, and the other is the anomaly-based that depends on machine learning to detect intrusions. They have a complementary effect to avoid missing some attacks. The ultimate trait of our proposed framework is that it is flexible enough for users to do some changes and improvements. Users just need to take surprisingly little effort to customize the framework to fit for their needs. We have designed an experiment to test the framework's ability to protect the simulated Web application against the brute force attack. The experimental results show that our intelligent framework has good performance and is able to detect the brute force attack timely.