When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification. (arXiv:2206.08004v1 [cs.CR])

Web: http://arxiv.org/abs/2206.08004

Internet traffic classification is widely used to facilitate network
management. It plays a crucial role in Quality of Services (QoS), Quality of
Experience (QoE), network visibility, intrusion detection, and traffic trend
analyses. While there is no theoretical guarantee that deep learning (DL)-based
solutions perform better than classic machine learning (ML)-based ones,
DL-based models have become the common default. This paper compares well-known
DL-based and ML-based models and shows that in the case of malicious traffic
classification, state-of-the-art DL-based solutions do …

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