{"_buckets": {"deposit": "d00d9f87-46e1-4c4b-b7e3-6a2374a5ed63"}, "_deposit": {"id": "3112", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3112"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3112", "sets": ["user-ytu"]}, "communities": ["ytu"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Association Rule Pattern Mining Approaches Network Anomaly  Detection", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "\u003cp\u003eThe research area for intrusion detection is becoming growth with new challenges of attack day by\u003cbr\u003e\nday.\u0026nbsp; Intrusion\u0026nbsp; detection\u0026nbsp; system\u0026nbsp; includes\u0026nbsp; identifying\u0026nbsp; a\u0026nbsp; set\u0026nbsp; of\u0026nbsp; malicious\u0026nbsp; actions\u0026nbsp; that\u0026nbsp; compromise\u0026nbsp; the\u0026nbsp; integrity,\u003cbr\u003e\nconfidentiality,\u0026nbsp; and\u0026nbsp; availability\u0026nbsp; of\u0026nbsp; information\u0026nbsp; resources.\u0026nbsp; The\u0026nbsp; major\u0026nbsp; objective\u0026nbsp; of\u0026nbsp; this\u0026nbsp; paper\u0026nbsp; is\u0026nbsp; to\u0026nbsp; apply\u003cbr\u003e\nassociation rule pattern mining approaches for network intrusion detection system. In this paper, traditional FP-\u003cbr\u003e\ngrowth algorithm, one of the association algorithms is modified and used to mine itemsets from large database.\u003cbr\u003e\nThe required statistics from large databases are gathered into a smaller data structure (FP-tree). The itemsets\u003cbr\u003e\ngenerated from FP-tree are used as profiles to check anomaly detection in the proposed system.\u003c/p\u003e"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "data mining"}, {"interim": "intrusion"}, {"interim": "anomaly"}, {"interim": "frequent itemset"}, {"interim": "algorithm"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-04"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Association Rule Pattern Mining Approaches Network Anomaly Detection.pdf", "filesize": [{"value": "219 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 219000.0, "url": {"url": "https://meral.edu.mm/record/3112/files/Association Rule Pattern Mining Approaches Network Anomaly Detection.pdf"}, "version_id": "3cde69b6-4d93-42ae-8f99-3f9e1a1f8326"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICFCT\u00272015", "subitem_c_date": "29-30 March, 2015", "subitem_conference_title": "Proceedings of 2015 International Conference on Future Computational Technologies  (ICFCT\u00272015)", "subitem_part": "", "subitem_place": "Singapore", "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": "Khin Moh Moh Aung"}, {"subitem_authors_fullname": "Nyein Nyein Oo"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2015-03-30"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.5281/zenodo.3268373"}, "item_title": "Association Rule Pattern Mining Approaches Network Anomaly  Detection", "item_type_id": "21", "owner": "1", "path": ["1596119372420"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003112", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-04"}, "publish_date": "2019-07-04", "publish_status": "0", "recid": "3112", "relation": {}, "relation_version_is_last": true, "title": ["Association Rule Pattern Mining Approaches Network Anomaly  Detection"], "weko_shared_id": -1}