Research Of Intrusion Detection Method Based On Rough Set And Adaptive Boost

As the development of computer network especially the Internet techniques, network is playing more and more important roles in our daily life as well as work.With nodes of the vast network, more and more sensitive information is being stored and manipulated online, the computer network is more vulnerable to miscellaneously malicious or unauthorized actions, network security is becoming increasingly important.The traditional network security techniques, which are passive defense, such as IA(Identification and Authentication), Firewall and so on, can't already satisfy the need of network security. The intrusion detection, which is active defense technique, has become another important means and the second floor defense in network security. Nowadays,Intrusion detection has become a hot point domain of research.To solve the problem of high rate of false negatives and false positives of IDS, an intrusion detection method was proposed in this paper which combines rough set and adaboost algorithm. Rough set is used to reduce amount of Adaboost'training data and improve running speed. Adaboost is a learning algorithm for constructing accurate classifiers. It can obtain a strong learning algorithm by combining a series of weak learning algorithms through some rules.The result of the experiment shows that this model has high detection rate and detection efficiency.The main works of this dissertation are summarized as follows:1.The principle theory of intrusion detection are introduced in this paper,and several intrusion detection methods are analyzed.2.Rough set theory basic concepts are introduced in this paper. Attribute reduction and attribute value reduction were studie based on the rough set theory.3.The thesis researched on Adaboost makes some improvement to satisfy the needof intrusion detection.