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Erschienen in:
Adversarial Attack, Defense, and Applications with Deep Learning Frameworks Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Using Convolutional Neural Networks for Classifying Malicious Network Traffic

verfasst von : Kyle Millar, Adriel Cheng, Hong Gunn Chew, Cheng-Chew Lim

Erschienen in: Deep Learning Applications for Cyber Security

Verlag: Springer International Publishing

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Abstract

As the reliance on the Internet and its constituent applications increase, so too does the value in exploiting these networking systems. Methods to detect and mitigate these threats can no longer rely on singular facets of information, they must be able to adapt to new threats by learning from a diverse range of information. For its ability to learn complex inferences from large data sources, deep learning has become one of the most publicised techniques of machine learning in recent years. This chapter aims to investigate a deep learning technique typically used for image classification, the convolutional neural network (CNN), and how its methodology can be adapted to detect and classify malicious network traffic.

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Vorheriges Kapitel Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks
Nächstes Kapitel DBD: Deep Learning DGA-Based Botnet Detection
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Metadaten
Titel
Using Convolutional Neural Networks for Classifying Malicious Network Traffic
verfasst von
Kyle Millar
Adriel Cheng
Hong Gunn Chew
Cheng-Chew Lim
Copyright-Jahr
2019
Buch
Deep Learning Applications for Cyber Security

Print ISBN: 978-3-030-13056-5

Electronic ISBN: 978-3-030-13057-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-13057-2_5

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