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Type: Conference paper
Title: Looking deeper: Using deep learning to identify internet communications traffic
Author: Millar, K.
Smit, D.
Page, C.
Cheng, A.
Chew, H.
Lim, C.
Citation: Proceedings of the 2017 Australasian Conference of Undergraduate Research, as published in Macquarie Matrix: Special edition, ACUR 2017, 2017 / vol.6.1, pp.124-144
Publisher: Macquarie University
Issue Date: 2017
Series/Report no.: Macquarie Matrix: Undergraduate Research Journal; 6.1
ISSN: 1839-5163
Conference Name: Australasian Conference of Undergraduate Research (27 Sep 2017 - 28 Sep 2017 : Adelaide, Australia)
Statement of
Daniel Smit, Kyle Millar, Clinton Page, Adriel Cheng, Hong-Gunn Chew and Cheng-Chew Lim
Abstract: Recent years have shown an unprecedented reliance on the internet to provide services essential for business, education, and personal use. Due to this reliance, coupled with the exponential growth of the internet traffic being generated, there has never been a greater necessity for effective network management techniques. Network traffic classification is one key component of this network management which aims to identify the types and quantity of traffic flowing through a network. Previous traffic classification techniques are limited by the use of non-standardised port numbers and the encryption of traffic contents. To tackle these challenges, we propose using deep learning techniques for network traffic classification. This paper investigates the viability of using deep learning for traffic classification with a focus on both network management applications and detecting malicious traffic. Our preliminary results thus far show that a highly accurate classifier can be created using the first 50 bytes of a traffic flow.
Keywords: deep learning; internet traffic classification; artificial neural networks; network security
Rights: © the author(s). Open Access. All works published are under an attribution, non-commercial, and share-alike Creative Commons licence. Attribution-NonCommercial-ShareAlike 1.0 Generic (CC BY-NC-SA 1.0)
RMID: 0030081838
Published version:
Appears in Collections:Electrical and Electronic Engineering publications

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