  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 not necessarily
outperform the classical ML-based ones. We exemplify this finding using two
well-known datasets for a varied set of tasks, such as: malware detection,
malware family classification, detection of zero-day attacks, and
classification of an iteratively growing dataset. Note that, it is not feasible
to evaluate all possible models to make a concrete statement, thus, the above
finding is not a recommendation to avoid DL-based models, but rather empirical
proof that in some cases, there are more simplistic solutions, that may perform
even better.
