[Colloquium] REMINDER Xi Jiang’s MS Presentation/ Nov.17th,2023

[Devin Davis]

This is an announcement of Xi Jiang’s MS Presentation
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Candidate: Xi Jiang

Date: November 17, 2023

Time: 9am

Location: JCL 298

M.S. Paper Title: AC-DC: Adaptive Ensemble Classification for Network Traffic Identification

Abstract: Accurate and efficient network traffic classification is important for many network management tasks, from traffic prioritization to anomaly detection. Although classifiers using pre-computed flow statistics (e.g., packet sizes, inter-arrival times) can be efficient, they often result in lower accuracy compared with techniques based on raw traffic. Conversely, past work on representation learning-based classifiers applied to network traffic captures has been shown to be highly accurate, yet slow and requiring considerable additional system resources (e.g., memory), due to the substantial costs in feature preprocessing. In this paper, we explore this trade-off and develop the Adaptive Constraint-Driven Classification (ACDC) framework to efficiently curate a pool of classifiers with different target requirements, aiming to provide comparable classification performance to complex packet-capture classifiers while adapting to varying network traffic load. AC-DC uses an adaptive scheduler that tracks current system resource availability and traffic rates to determine a balanced classifier and batch size to maximize classification performance given system and processing constraints. Our evaluation shows that AC-DC improves classification performance by over 18% compared to classifiers that rely on flow statistics alone; when benchmarked against state-of-the-art packet-capture classifiers, AC-DC results in a worst-case drop in F1-Score of only 12.3% while processing traffic ∼50x faster.

Advisors: Nick Feamster

Committee Members: Nick Feamster, Grant Ho, Sanjay Krishnan

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