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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Malicious-Traffic Classification Using Deep Learning with Packet Bytes and Arrival Time
verfasst von : Ingyom Kim, Tai-Myoung Chung
Erschienen in: Future Data and Security Engineering
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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.