New Directions in Automated Traffic Analysis

Abstract

Despite the use of machine learning for many network traffic analysis tasks in security, from application identification to intrusion detection, the aspects of the machine learning pipeline that ultimately determine the performance of the model -- feature selection and representation, model selection, and parameter tuning -- remain manual and painstaking. This paper presents a method to automate many aspects of traffic analysis, making it easier to apply machine learning techniques to a wider variety of traffic analysis tasks. We introduce nPrint, a tool that generates a unified packet representation that is amenable for representation learning and model training. We integrate nPrint with automated machine learning (AutoML), resulting in nPrintML, a public system that largely eliminates feature extraction and model tuning for a wide variety of traffic analysis tasks. We have evaluated nPrintML on eight separate traffic analysis tasks and released nPrint and nPrintML to enable future work to extend these methods.

References in corpus (1)

• Automated Website Fingerprinting through Deep Learning

Cited by in corpus (4)

• AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification
• Mapping the Landscape of Generative AI in Network Monitoring and Management
• FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets
• Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams