2026-07-28T17:21:09Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/3112 2021-12-13T00:37:39Z 1582963413512:1596119372420 user-ytu

Association Rule Pattern Mining Approaches Network Anomaly Detection Khin Moh Moh Aung Nyein Nyein Oo <p>The research area for intrusion detection is becoming growth with new challenges of attack day by<br> day.&nbsp; Intrusion&nbsp; detection&nbsp; system&nbsp; includes&nbsp; identifying&nbsp; a&nbsp; set&nbsp; of&nbsp; malicious&nbsp; actions&nbsp; that&nbsp; compromise&nbsp; the&nbsp; integrity,<br> confidentiality,&nbsp; and&nbsp; availability&nbsp; of&nbsp; information&nbsp; resources.&nbsp; The&nbsp; major&nbsp; objective&nbsp; of&nbsp; this&nbsp; paper&nbsp; is&nbsp; to&nbsp; apply<br> association rule pattern mining approaches for network intrusion detection system. In this paper, traditional FP-<br> growth algorithm, one of the association algorithms is modified and used to mine itemsets from large database.<br> The required statistics from large databases are gathered into a smaller data structure (FP-tree). The itemsets<br> generated from FP-tree are used as profiles to check anomaly detection in the proposed system.</p> 2015-03-30 http://hdl.handle.net/20.500.12678/0000003112 https://meral.edu.mm/records/3112