%0 Conference Proceedings %1 mohmohaung2015association %A Aung, Khin Moh Moh %A Oo, Nyein Nyein %D 2015 %K anomaly association masterarbeit mining pattern rules %T Association Rule Pattern Mining Approaches Network Anomaly Detection %U http://urst.org/siteadmin/upload/7032U0315248.pdf %X The research area for intrusion detection is becoming growth with new challenges of attack day by day. Intrusion detection system includes identifying a set of malicious actions that compromise the integrity, confidentiality, and availability of information resources. The major objective of this paper is to apply association rule pattern mining approaches for network intrusion detection system. In this paper, traditional FP-growth algorithm, one of the association algorithms is modified and used to mine itemsets from large database. The required statistics from large databases are gathered into a smaller data structure (FP-tree). The itemsets generated from FP-tree are used as profiles to check anomaly detection in the proposed system.

@proceedings{mohmohaung2015association, abstract = {The research area for intrusion detection is becoming growth with new challenges of attack day by day. Intrusion detection system includes identifying a set of malicious actions that compromise the integrity, confidentiality, and availability of information resources. The major objective of this paper is to apply association rule pattern mining approaches for network intrusion detection system. In this paper, traditional FP-growth algorithm, one of the association algorithms is modified and used to mine itemsets from large database. The required statistics from large databases are gathered into a smaller data structure (FP-tree). The itemsets generated from FP-tree are used as profiles to check anomaly detection in the proposed system. }, added-at = {2016-07-04T12:39:41.000+0200}, author = {Aung, Khin Moh Moh and Oo, Nyein Nyein}, biburl = {https://www.bibsonomy.org/bibtex/2ed960cd135b9ddb51faf69bf093aabea/wipster}, interhash = {d12b03db8b67fe70cbc977c2dba27200}, intrahash = {ed960cd135b9ddb51faf69bf093aabea}, keywords = {anomaly association masterarbeit mining pattern rules}, timestamp = {2016-07-05T13:26:29.000+0200}, title = {Association Rule Pattern Mining Approaches Network Anomaly Detection}, url = {http://urst.org/siteadmin/upload/7032U0315248.pdf}, year = 2015 }