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New Index
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Item
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Network Anomaly Detection using Threshold-based Sparse Autoencoder
http://hdl.handle.net/20.500.12678/0000002935
http://hdl.handle.net/20.500.12678/0000002935add4e4d3-d0e8-4092-96dc-5e2f7236038b
8a3e6feb-2c12-46fc-971e-bb764a70157d
Publication type | ||||||
---|---|---|---|---|---|---|
Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Network Anomaly Detection using Threshold-based Sparse Autoencoder | |||||
Language | en | |||||
Publication date | 2020-07-03 | |||||
Authors | ||||||
May Thet Tun | ||||||
Dim En Nyaung | ||||||
Myat Pwint Phyu | ||||||
Description | ||||||
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. | ||||||
Keywords | ||||||
Network anomaly detection, Deep learning, Sparse autoencoder, Percentile threshold approach, Chi-squared feature selection | ||||||
Identifier | 10.1145/3406601.3406626 | |||||
Conference papers | ||||||
IAIT 2020 | ||||||
1-3 July, 2020 | ||||||
Proceedings of the 11th International Conference on Advances in Information Technology | ||||||
Bangkok, Thailand | ||||||
http://www.iait-conf.org/2020/ |