INTRUSION DETECTION SYSTEM USING DECISION TREE AND APRIORI ALGORITHM

Intrusion Detection System (IDS) has become important mechanism to protect the network. Data mining techniques makes it possible to search large amount of data for characteristics, rules and patterns. It helps to network for detecting intrusion and attacks. Here, we present intrusion detection model based on Decision Tree algorithm and Apriori clustering algorithm. Both Algorithms of Data Mining in Intrusion Detection System are able to predict new type of attacks based on the training data sets. Hence, data mining is important approach that is used in IDS (Intrusion Detection System). Previously, data mining based network intrusion detection system was giving accuracy and good detection on different types of attacks. In this paper, the performance of the data mining algorithms improved C5.0 are being used in order to detect the different types of attacks with high accuracy and less error prone as well as it helps to increase performance of the system.