{"_buckets": {"deposit": "dcf2bbbb-1ea2-444c-a710-c53d1fda23b5"}, "_deposit": {"id": "3251", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3251"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3251", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Detecting P2P Botnets Network Traffic Behaviors Using Feature-Based Learning Techniques", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Botnets have become one of the majorthreats on the Internet. They are used to generatespam, carry out DDOS (Distributed Denial ofService) attacks and click-fraud, and steal sensitiveinformation. Nowadays, many researchers interest toanalyze the botnet technology and emphasis thebotnet behaviors. It is needed to classify communicationnetwork traffic which is important fact tostudy the botnet behaviors. In this paper, we proposedan approach to detect botnet activity by analyzing andclassifying network traffic behaviors due to P2P (Peerto Peer) based botnets. This system represents theimportant and most challenging types of botnetcurrently available that based on classifying P2Pbotnets. The classification techniques used indetection framework are RF (Random Forest) andSVM (Support Vector Machine). The performanceevaluation of the two popular classificationtechniques is also presented. According to theexperiments, proposed system has promising accuracyeven with small time window by comparing twomachine learning algorithms."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Botnets"}, {"interim": "Machine Learning"}, {"interim": "HTTP"}, {"interim": "IRC"}, {"interim": "P2P"}, {"interim": "Waledac"}, {"interim": "Storm"}, {"interim": "RF"}, {"interim": "SVM"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "12027.pdf", "filesize": [{"value": "310 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 310000.0, "url": {"url": "https://meral.edu.mm/record/3251/files/12027.pdf"}, "version_id": "77063f17-43d3-4e5d-8259-0b29aa89df1c"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Twelfth International Conference On Computer Applications (ICCA 2014)", "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": "Thu, Aye Aye"}, {"subitem_authors_fullname": "Mya, Khin Than"}]}]}, "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": "2014-02-17"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/102"}, "item_title": "Detecting P2P Botnets Network Traffic Behaviors Using Feature-Based Learning Techniques", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003251", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "3251", "relation": {}, "relation_version_is_last": true, "title": ["Detecting P2P Botnets Network Traffic Behaviors Using Feature-Based Learning Techniques"], "weko_shared_id": -1}