A Design and Implementation of New Hybrid System for Anomaly Intrusion Detection System to Improve EfficiencyAbstract: All most all-existing intrusion detection systems focus on attacks at low-level, and only produced isolated alerts. It is known that existing IDS can't find any type of logical relations among alerts. In addition, they counted very low in accuracy; lots of alerts are false. Proposed research is a combination of three data mining technique to reduce false alarm rate in intrusion detection system that is known a hybrid intrusion detection system (HIDS) combining k-Means (KM), K-nearest neighbor (KNN) and Decision Table Majority (DTM) (rule based) approaches for anomaly detection. Proposed HIDS operates on the KDD-99 Data set; this data set is used worldwide for evaluating the performance of different intrusion detection systems. Initially clusteringperformed via k-Means on KDD99 (knowledge Discovery and Data Mining) intrusion detection after that we apply two-classification techniques; KNN which is followed by DTM. The Proposed system can detect the intrusions and classify them into four categories: R2L (Remote to Local), Denial of Service (DoS), Probe and U2R (User to Root). The prime concern of the proposed concept is to decrease the IDS false alarm rate and increase the accuracy and detection rate.
Key words: Association Analysis, Clustering,Data Mining, Data Preprocessing,Intrusion Detection
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