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|Title:||Deep learning for classifying malicious network traffic|
|Citation:||Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018), as published in Trends and Applications in Knowledge Discovery and Data Mining, 2018 / vol.11154, pp.156-161|
|Series/Report no.:||Lecture Notes in Computer Science; 11154|
|Conference Name:||22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (03 Jun 2018 - 06 Jun 2018 : Melbourne, Australia)|
|K. Millar, A. Cheng, H. G. Chew, and C.-C. Lim|
|Abstract:||As the sophistication of cyber malicious attacks increase, so too must the techniques used to detect and classify such malicious traffic in these networks. Deep learning has been deployed in many application domains as it is able to learn patterns from large feature sets. Given that the implementation of deep learning for network traffic classification is only just starting to emerge, the question of how best to utilise and represent network data to such a classifier still remains. This paper addresses this question by devising and evaluating three different ways of representing data to a deep neural network in the context of malicious traffic classification. We show that although deep learning does not show significant improvement over other machine learning techniques using metadata features, its use on payload data highlights the potential for deep learning to be incorporated into novel deep packet inspection techniques. Furthermore, we show that useful predictions of malicious classes can still be made when the input is limited to just the first 50 bytes of a packet’s payload.|
|Keywords:||Deep learning; Convolutional neural networks; Internet traffic classification; Malicious traffic detection|
|Rights:||© Springer Nature Switzerland AG 2018|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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