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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Millar, K., Cheng, A., Chew, H.G., Lim, CC. (2019). Using Convolutional Neural Networks for Classifying Malicious Network Traffic. In: Alazab, M., Tang, M. (eds) Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-13057-2_5
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