{"_buckets": {"deposit": "7779be93-6da4-4e7b-b59c-2771b8c248e9"}, "_deposit": {"id": "4468", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4468"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4468", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine", "subitem_1551255648112": "en_US"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Due to continuous growth of the Internettechnology, it needs to establish securitymechanism. However, many current intrusiondetection systems (IDSs) are rule-based systems,which have limitations to detect novel intrusions.Moreover, encoding rules is time-consuming andhighly depends on the knowledge of knownintrusions. Therefore, we propose new systematicframework that apply a data mining algorithmcalled random forests (RF) and Support VectorMachine (SVM). This system uses RandomForests (RF) for feature selection and parameteroptimization and Support Vector Machine (SVM)for intrusion detection. RF provides the variableimportance by numeric values so that theirrelevant features can be eliminated. SupportVector Machines (SVM) as a classical patternrecognition tool have been widely used forintrusion detection. First, RF is utilized topreprocess the data and select the mostimportant features to eliminate the insignificantfeatures and optimize parameters. Second, SVMmodel is used to learn and detect intrusion usingselected important features."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Intrusion Detection Systems"}, {"interim": "Random Forests"}, {"interim": "Support Vector Machine"}, {"interim": "Feature Selection"}, {"interim": "Parameter Optimization"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-11-13"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "10040.pdf", "filesize": [{"value": "421 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 421000.0, "url": {"url": "https://meral.edu.mm/record/4468/files/10040.pdf"}, "version_id": "47d93837-dc3a-4633-b96a-5490e38da59f"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Tenth International Conference On Computer Applications (ICCA 2012)", "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": "Oo, May Mar"}, {"subitem_authors_fullname": "Yi, Aye Mon"}]}]}, "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": "2012-02-28"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/2398"}, "item_title": "A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004468", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-11-13"}, "publish_date": "2019-11-13", "publish_status": "0", "recid": "4468", "relation": {}, "relation_version_is_last": true, "title": ["A Framework for Intrusion Detection System Using Random Forests and Support Vector Machine"], "weko_shared_id": -1}