{"created":"2020-09-01T15:40:02.107171+00:00","id":5036,"links":{},"metadata":{"_buckets":{"deposit":"4ea5ec49-9f79-4a1c-9f2c-6684de2b9aa0"},"_deposit":{"id":"5036","owners":[],"pid":{"revision_id":0,"type":"recid","value":"5036"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/5036","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Single-Linkage Clustering Approach for Kdd Dataset","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"This paper presents a type of clusteringbasedintrusion detection using single-linkageclustering algorithm.Basic methods for clusteringinclude the Linkage based and K-meanstechniques.The K-means method generallyproduces a more accurate clustering than linkagebased methods, but it has a greater timecomplexity and this becomes an extremelyimportant factor in network intrusion detectiondue to very large dataset sizes.Intrusions pose aserious security risk in a network environment.Although systems can be hardened against manytypes of intrusions, often intrusions aresuccessfulmaking systems for detecting these intrusionscritical to the security of these system. Newintrusion types, of which detection systems areunaware, are the most difficult to detect. Singlelinkageclustering-based intrusion detectionmethod is able to detect many different types orintrusions, while maintaining a low false positiverate as verified over the KDD CUP 1999 dataset."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-18"}],"displaytype":"preview","filename":"26_PDFsam_PSC_final proof.pdf","filesize":[{"value":"3 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/5036/files/26_PDFsam_PSC_final proof.pdf"},"version_id":"ee19b58b-e728-49a8-a429-f3466f2c3ed9"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Eighth Local Conference on Parallel and Soft Computing","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Myint, Yu Mon"},{"subitem_authors_fullname":"Khaing, Ma"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2017-12-27"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/930"},"item_title":"Single-Linkage Clustering Approach for Kdd Dataset","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-18","publish_status":"0","recid":"5036","relation_version_is_last":true,"title":["Single-Linkage Clustering Approach for Kdd Dataset"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2022-03-24T23:14:15.049643+00:00"}