Network Anomaly Detection using Threshold-based Sparse Autoencoder

May Thet Tun; Dim En Nyaung; Myat Pwint Phyu

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Network Anomaly Detection using Threshold-based Sparse Autoencoder

http://hdl.handle.net/20.500.12678/0000002935

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/