2026-05-01T11:12:07Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/3113 2021-12-13T05:46:30Z 1582963413512:1596119372420 user-ytu

Intrusion Detection System Based on Frequent Pattern Mining Myo Min Than Nyein Nyein Oo Myo Min Than <p>&nbsp;Due&nbsp; to&nbsp; the&nbsp; dramatically&nbsp; increment&nbsp; of&nbsp; internet<br> usage,&nbsp; users&nbsp; are&nbsp; facing&nbsp; various&nbsp; attacks&nbsp; day&nbsp; by&nbsp; day.<br> Consequently, the research area for intrusion detection must<br> be&nbsp; fresh&nbsp; with&nbsp; new&nbsp; challenges.&nbsp; Intrusion&nbsp; detection&nbsp; system<br> includes identifying a set of malicious actions that compromise<br> the&nbsp; integrity,&nbsp; confidentiality,&nbsp; and&nbsp; availability&nbsp; of&nbsp; information<br> resources.&nbsp; The&nbsp; major&nbsp; contribution&nbsp; is&nbsp; to&nbsp; apply&nbsp; data&nbsp; mining<br> approach for network intrusion detection system. Among the<br> several features of data&nbsp; mining, association rules&nbsp; mining,FP-<br> growth algorithm, is used to find out the frequent itemsets of<br> incoming packets database. Based on these itemsets, anomaly<br> detection&nbsp; is&nbsp; added.&nbsp; The&nbsp; system&nbsp; will&nbsp; predict&nbsp; whether&nbsp; the<br> incoming data packet is normal or attack. The performance of<br> proposed system is tested by using KDD-99 datasets.</p> 2014-12-29 http://hdl.handle.net/20.500.12678/0000003113 https://meral.edu.mm/records/3113