A Monthly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories. International Research Journal of Mathematics, Engineering and IT (IRJMEIT) 6 | Page DATA MINING TECHNIQUES FOR INTRUSION DETECTION: A REVIEW 1 Shikha Attri and 2 R C Gangwar and 3 Rajeev Bedi 1 Post-Graduate Student, Computer Sc. & Engg, IKG Punjab Technical University, Kapurthala(Pb) India. 2 Associate Professor, Department of Computer Sc, Beant College of Engg. & Tech, Gurdaspur(Pb) India. 3 Assistant Professor, Department of Computer Sc, Beant College of Engg. & Tech, Gurdaspur(Pb) India. ABSTRACT With significant advancement of web, security of system activity is turning into a major issue PC system framework. Cyber attacks on system are expanding day-by-day. Intrusion is considered as most pitched attack on system traffic. Intrusion recognition framework has been utilized for finding out intrusion and to protect the security objectives of data from attacks. Data mining systems are utilized to screen and investigate extensive measure of system information and group this system information into anomalous and typical information. Since information originates from different sources, system traffic is substantial. Data mining methods such as classification and clustering are connected to design of intrusion detection framework. A viable Intrusion detection framework requires high recognition rate, low false caution rate and additionally high precision. This paper exhibits the audit on IDS and diverse Data mining methods connected on IDS for the powerful detection of pattern for both malicious and typical activities in the system, which creates secure data framework. This paper also presents two distinct clustering algorithms known as K-Means Clustering and Hierarchical Clustering Algorithm. K-Means clustering results indegeneracy and is not suitable for large databases. International Research Journal of Mathematics, Engineering and IT Vol. 3, Issue 9, September 2016 IF- 3.563 ISSN: (2349-0322) © Associated Asia Research Foundation (AARF) Website: www.aarf.asia Email : editor@aarf.asia , editoraarf@gmail.com