Finding Frequent Itemsets using Apriori Algorihm to Detect Intrusions in Large Dataset

With the growth of hacking and exploiting tools and invention of new ways of intrusion, Intrusion detection and prevention is becoming the major challenge in the world of network security. The increasing network traffic and data on Internet is making this task more demanding. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. The flase positive rates makes it extremely hard for to analyze and react to attacks. Intrusion detection systems using data mining approaches make it possible to search patterns and rules in large amount of audit data. In this paper, we represent an model to integrate association rules to intrusion detection to design and implement an network intrusion detection system. Our technnique is used to generate attack rules that will detect the attacks in network audit data using anomaly detection. This shows that the association rules mining algorithm is capable of detecting network intrusions. The KDD dataset which is freely available online is used for our experimentation and results are discussed. Our aim is to experiment with dfiiferent parameters of apriori algorithm to build a string intrusion detection system using association rule mining.