{"_buckets": {"deposit": "8a3e6feb-2c12-46fc-971e-bb764a70157d"}, "_deposit": {"created_by": 45, "id": "2935", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "2935"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/2935", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Network Anomaly Detection using Threshold-based Sparse Autoencoder", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Nowadays, cyber-attacks have been dramatically increased due to the rapid development of Internet-based services. The current network anomaly detection solutions such as Firewall, Snort, honeypots are useful, but they are insufficient to deal with zero-day attacks. At present, unsupervised deep learning technologies focus on increasing the performance of anomaly-based network attack detection in the recent research area. The normal and attack transactions can be distinguished by the threshold in the network anomaly detection using a sparse autoencoder. The threshold value estimation is not a non-trivial task. The key factor of the threshold value which draws a line between normal and attack highly depends on the accuracy of the network anomaly detection. In this paper, the network anomaly detection based on a sparse autoencoder with a percentile-based threshold selection algorithm is proposed. The results of the proposed system have been validated concerning the accuracy, precision, recall and false positive rate. As a result, the experimental results on UNSW-NB15 and NSL-KDD datasets have shown that the proposed system provides higher accuracy and outperforms the previous related works in recent years."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Network anomaly detection"}, {"interim": "Deep learning"}, {"interim": "Sparse autoencoder"}, {"interim": "Percentile threshold approach"}, {"interim": "Chi-squared feature selection"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_no", "date": [{"dateType": "Available", "dateValue": "2020-08-06"}], "displaytype": "preview", "filename": " Network Anomaly Detection using Threshold-based Sparse Autoencoder .pdf", "filesize": [{"value": "565 Kb"}], "format": "application/pdf", "licensefree": "© 2020 Association for Computing Machinery", "licensetype": "license_free", "url": {"url": "https://meral.edu.mm/record/2935/files/ Network Anomaly Detection using Threshold-based Sparse Autoencoder .pdf"}, "version_id": "a424c976-9410-4171-977a-f86baa9f69fb"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "IAIT 2020", "subitem_c_date": "1-3 July, 2020", "subitem_conference_title": "Proceedings of the 11th International Conference on Advances in Information Technology", "subitem_place": "Bangkok, Thailand", "subitem_website": "http://www.iait-conf.org/2020/"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "May Thet Tun"}, {"subitem_authors_fullname": "Dim En Nyaung"}, {"subitem_authors_fullname": "Myat Pwint Phyu"}]}]}, "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": "2020-07-03"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.1145/3406601.3406626"}, "item_title": "Network Anomaly Detection using Threshold-based Sparse Autoencoder", "item_type_id": "21", "owner": "45", "path": ["1596102355557"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000002935", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-08-06"}, "publish_date": "2020-08-06", "publish_status": "0", "recid": "2935", "relation": {}, "relation_version_is_last": true, "title": ["Network Anomaly Detection using Threshold-based Sparse Autoencoder"], "weko_shared_id": -1}