Deep Learning for Network Traffic Classification

2021-06-02 · Niloofar Bayat, Weston Jackson, Derrick Liu

Monitoring network traffic to identify content, services, and applications is an active research topic in network traffic control systems. While modern firewalls provide the capability to decrypt packets, this is not appealing for privacy advocates. Hence, identifying any information from encrypted traffic is a challenging task. Nonetheless, previous work has identified machine learning methods that may enable application and service identification. The process involves high level feature extraction from network packet data then training a robust machine learning classifier for traffic identification. We propose a classification technique using an ensemble of deep learning architectures on packet, payload, and inter-arrival time sequences. To our knowledge, this is the first time such deep learning architectures have been applied to the Server Name Indication (SNI) classification problem. Our ensemble model beats the state of the art machine learning methods and our up-to-date model can be found on github: \url{https://github.com/niloofarbayat/NetworkClassification}

📄 PDF Abstract BibTeX arXiv:2106.12693

Code (1)

niloofarbayat/NetworkClassification 공식 구현

Tasks

BIG-bench Machine Learning ClassificationDeep LearningTraffic Classification

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