{"created":"2020-08-06T14:42:41.001667+00:00","id":2935,"links":{},"metadata":{"_buckets":{"deposit":"8a3e6feb-2c12-46fc-971e-bb764a70157d"},"_deposit":{"created_by":45,"id":"2935","owner":"45","owners":[45],"owners_ext":{"displayname":"","email":"dimennyaung@uit.edu.mm","username":""},"pid":{"revision_id":0,"type":"recid","value":"2935"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/2935","sets":["1582963342780:1596102355557"]},"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":[]},"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"],"publish_date":"2020-08-06","publish_status":"0","recid":"2935","relation_version_is_last":true,"title":["Network Anomaly Detection using Threshold-based Sparse Autoencoder"],"weko_creator_id":"45","weko_shared_id":-1},"updated":"2021-12-13T00:30:10.599286+00:00"}