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2020 | OriginalPaper | Chapter

Malicious-Traffic Classification Using Deep Learning with Packet Bytes and Arrival Time

Authors : Ingyom Kim, Tai-Myoung Chung

Published in: Future Data and Security Engineering

Publisher: Springer International Publishing

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Abstract

Internet technology is rapidly developing through the development of computer technology. However, we haven been experiencing problems such as malware with these developments. Various methods of malware detection have been studied for years to respond to malicious codes. There are three main ways to classify traffic. They are port-based, payload-based and a machine learning method. We attempt to classify malicious traffic using CNN which is one of deep learning algorithms. The features we use for CNN are the packet’s size and its arrival time. The packet’s size and arrival time information are extracted and then converted into an image file. The converted image is then used for CNN to classify what type of attack the traffic is. The accuracy of the proposed technique was 95%, which showed very high performance, proving that classification was possible.

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Metadata
Title
Malicious-Traffic Classification Using Deep Learning with Packet Bytes and Arrival Time
Authors
Ingyom Kim
Tai-Myoung Chung
Copyright Year
2020
Book
Future Data and Security Engineering

Print ISBN: 978-3-030-63923-5

Electronic ISBN: 978-3-030-63924-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63924-2_20

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