Network Intrusion Detection Technology In Data Mining Algorithms

With the popularization of the applications of network-based computer systems and the increasing frequency of e-commerce, security issues become more and more outstanding. Intrusion detection system(IDS) plays important roles in the information security architecture. The computer criminal is more and more pressing and dangerous nowadays, which poses urgent demands on the performance of IDS. However, current intrusion detection systems lack effectiveness, adaptability and extensibility. Aimed at these shortcomings, this thesis takes a data-centric view to IDS and describes a framework for constructing intrusion detection model by mining audit data.This thesis first provided the background on IDS. We then provided the data mining knowledge and the applications in Intrusion Detection. By studying and analyzing the flaws of traditional IDS, we can know that we should deal with numerous data to solve these flaws. The Data Mining technology is exactly strongly data-dealing tool. So through using the Data Mining technology into IDS to deal with the numerous data, we can improve the detect-ability of the whole IDS, and reduce its fake alert and error alert.It is researched that Model for Intrusion Detection with Data Mining Technology and we develop a framework that facilitates automatic and systematic construction of IDS. We focus on the association and clustering algorithm and by analyzing the existent flaws which the Apriori algorithm and K-means clustering algorithm using into IDS have, we improve on these flaws with examples to prove that the improvement is effective.