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Visualising Network Anomalies in an Unsupervised Manner Using Deep Network Autoencoders

Authors:
Matthew Banton
Nathan Shone
William Hurst
Qi Shi

Keywords: Autoencoder, Visualisation, k-NN, Deep Learning

Abstract:
As network data continues to grow in volume, it is important that network administrators have the tools to be able to identify anomalous network flows and malicious activity. However, it is just as important that tools allow the administrator to visualise this activity in relation to other benign activity. As such, this paper will propose a method to not only identify malicious activity, but also visualise the activity and how it relates to other network activity (both benign and malicious).

Pages: 25 to 30

Copyright: Copyright (c) IARIA, 2019

Publication date: June 30, 2019

Published in: conference

ISSN: 2519-8645

ISBN: 978-1-61208-724-5

Location: Rome, Italy

Dates: from June 30, 2019 to July 4, 2019