Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Deep learning for classifying malicious network traffic
Author: Millar, K.A.
Cheng, A.
Chew, H.G.
Lim, C.C.
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
Publisher: Springer
Publisher Place: Switzerland
Issue Date: 2018
Series/Report no.: Lecture Notes in Computer Science; 11154
ISBN: 3030045021
ISSN: 0302-9743
Conference Name: 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (03 Jun 2018 - 06 Jun 2018 : Melbourne, Australia)
Statement of
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
RMID: 0030106170
DOI: 10.1007/978-3-030-04503-6_15
Published version:
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.