{"created":"2020-08-30T13:55:59.294612+00:00","id":3113,"links":{},"metadata":{"_buckets":{"deposit":"dbb7a1f5-b026-49ef-b5b8-9499b3d9def8"},"_deposit":{"id":"3113","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3113"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3113","sets":["1582963413512:1596119372420"]},"communities":["ytu"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Intrusion Detection System Based on Frequent Pattern Mining","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"
Due to the dramatically increment of internet
\nusage, users are facing various attacks day by day.
\nConsequently, the research area for intrusion detection must
\nbe fresh with new challenges. Intrusion detection system
\nincludes identifying a set of malicious actions that compromise
\nthe integrity, confidentiality, and availability of information
\nresources. The major contribution is to apply data mining
\napproach for network intrusion detection system. Among the
\nseveral features of data mining, association rules mining,FP-
\ngrowth algorithm, is used to find out the frequent itemsets of
\nincoming packets database. Based on these itemsets, anomaly
\ndetection is added. The system will predict whether the
\nincoming data packet is normal or attack. The performance of
\nproposed system is tested by using KDD-99 datasets.