A Python toolkit and initial benchmark package that turns SCION path measurements into ML-ready datasets, with baseline models for forecasting, failure prediction, anomaly detection, path recommendation, and bottleneck localization.
New Directions in Automated Traffic Analysis
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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.
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SCION Path Performance Toolkit and Benchmark for Advancing Machine Learning in Next-Generation Networks: ScionPathML
A Python toolkit and initial benchmark package that turns SCION path measurements into ML-ready datasets, with baseline models for forecasting, failure prediction, anomaly detection, path recommendation, and bottleneck localization.