{"_buckets": {"deposit": "99585b9b-7985-4889-b3b2-b3cdb5f11f75"}, "_deposit": {"created_by": 31, "id": "5529", "owner": "31", "owners": [31], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5529"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5529", "sets": ["user-ytu"]}, "communities": ["ytu"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Intrusion Detection System (IDS) for Alerting and Classifying Network Attacks", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Intrusion Detection System (IDS) is a useful defense technique against network\nattacks as well host attacks because they can help network/host administrator to detect\nany security violations by showing alerts. Although IDSs can produce thousands of alerts\nper day for network security, most of them are false positives. The abundance of false\npositive alerts can be weak for administrator to find successful attacks and give action on\nthem. The system is implemented to be accurate in IDS by classifying network attacks\nwith the created dataset. And also, the attack classification is tested by using both off-line\nand on-line alerts from the IDS. Then, the calculation of false alarm rate, the accuracy of\nthe system and the Percentage of Successful Prediction (PSP) are presented to be a good\nIDS system by reducing the workload of human analyst while classifying network attacks."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Intrusion Detection System (IDS)"}, {"interim": "attack classification"}, {"interim": "false alarm rate"}, {"interim": "accuracy"}, {"interim": "Percentage of Successful Prediction (PSP)"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"date": [{"dateType": "Available", "dateValue": "2020-09-28"}], "download_preview_message": "", "file_order": 0, "filename": "YTU Journal 2015.pdf", "filesize": [{"value": "679 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 679000.0, "url": {"url": "https://meral.edu.mm/record/5529/files/YTU Journal 2015.pdf"}, "version_id": "55a51a37-0946-4bbc-b6b1-abf8b5fedb2d"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "No. 4", "subitem_journal_title": "YTU Journal of Engineering", "subitem_pages": "68-75", "subitem_volume": "Volume 2"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "La Wunn Yee"}, {"subitem_authors_fullname": "Thanda Win"}, {"subitem_authors_fullname": "Htway Htway Hlaing"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2015-10-15"}, "item_title": "Intrusion Detection System (IDS) for Alerting and Classifying Network Attacks", "item_type_id": "21", "owner": "31", "path": ["1596119372420"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005529", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-25"}, "publish_date": "2020-09-25", "publish_status": "0", "recid": "5529", "relation": {}, "relation_version_is_last": true, "title": ["Intrusion Detection System (IDS) for Alerting and Classifying Network Attacks"], "weko_shared_id": -1}