Network Intrusion Detection Using Association Rules

Network intrusion detection includes identifying a set of malicious actions that compromise the integrity, confidentiality, and availability of information resources. The tremendous increase of novel cyber attacks has made data mining based intrusion detection techniques extremely useful in their detection. This paper describes a system that is able to detect network intrusion using association rules. The technique is used to generate attack rules that will detect the attacks in network audit data using anomaly detection. This shows that the modified association rules algorithm is capable of detecting network