A Design and Implementation of Intrusion Detection System by Using Data Mining

The role of the intrusion detection system is to enforce the pattern matching policies decided for the network. Basically Proposed IDS executes on the KDD´99 Data set, this data set is used in international level for evaluating/calculating the performance of various intrusion detection systems (IDS). First step is association phase in which frequent item set are produced by apriori algorithm. The second step is clustering phase in which clusters are created by k-Means. Proposed technique uses the standard KDD99 (knowledge Discovery and Data Mining) intrusion detection contest data set. Proposed system can detect the attacks/intrusions and classifies them into different categories: U2R (User to Root), probe, R2L (Remote to Local), and Denial of Service (DoS). The prime task of the proposed IDS is to improve effectiveness with efficiency. An experiment is carried out to evaluate/calculate the performance of the proposed approach using KDD 99´ data set. Here the result shows that the proposed IDS technique performs better in term of efficiency(Execution Speed) & effectiveness.