Research On Network Intrusion Detection Based On Association Rules

Network Intrusion Detection System(IDS), as the main security defending technique, is second guard for a network after firewall. Since it can discern and respond to the hostile behavior of the computer and network resource, it is a hot area for research network security nowadays. Data mining technology is applied to the network intrusion detection, and precision of the detection will be improved by the superiority of data mining.This paper introduces the current situations of the network security and intrusion detection at first, and the data mining technology. The paper also introduces the knowledge of data mining and method that can be used in intrusion detection.In order to give the original algorithm some proper expands, and apply the hash-prune algorithm to system, the algorithm of association rules in the network intrusion detection based on data mining technology is studied.The network intrusion detection system based on data mining(DMNIDS) is designed and realized . This system is constituted with the rule-made module and the real-time detection module. The rule sets of the system include normal behavior rules and abnormal behavior rules, it make the system can carry out the anomaly detection and misuse detection in theory. The system also has self-study ability. The experiment results indicate the system possesses high precision.