Generic Multi-modal Representation Learning for Network Traffic Analysis
arxiv(2024)
摘要
Network traffic analysis is fundamental for network management,
troubleshooting, and security. Tasks such as traffic classification, anomaly
detection, and novelty discovery are fundamental for extracting operational
information from network data and measurements. We witness the shift from deep
packet inspection and basic machine learning to Deep Learning (DL) approaches
where researchers define and test a custom DL architecture designed for each
specific problem. We here advocate the need for a general DL architecture
flexible enough to solve different traffic analysis tasks. We test this idea by
proposing a DL architecture based on generic data adaptation modules, followed
by an integration module that summarises the extracted information into a
compact and rich intermediate representation (i.e. embeddings). The result is a
flexible Multi-modal Autoencoder (MAE) pipeline that can solve different use
cases. We demonstrate the architecture with traffic classification (TC) tasks
since they allow us to quantitatively compare results with state-of-the-art
solutions. However, we argue that the MAE architecture is generic and can be
used to learn representations useful in multiple scenarios. On TC, the MAE
performs on par or better than alternatives while avoiding cumbersome feature
engineering, thus streamlining the adoption of DL solutions for traffic
analysis.
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