Authors: Gerry Wan, Shinan Liu, Francesco Bronzino, Nick Feamster, Zakir Durumeric

Published on: February 08, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.06099

Summary

• What is new: CATO integrates multi-objective Bayesian optimization for both predictive performance and system costs, offering a novel approach in ML-based network traffic analysis.
• Why this is important: Existing ML models for network traffic analysis are difficult to deploy due to high operational costs and inefficiencies when scaling.
• What the research proposes: The introduction of CATO, a framework that optimizes both the model’s performance and the system’s operational costs, enabling efficient real-time deployment.
• Results: CATO achieved up to 3600x lower inference latency and 3.7x higher zero-loss throughput with improved model performance compared to existing methods.

Technical Details

Technological frameworks used: Multi-objective Bayesian optimization, automated pipeline compilation

Models used: Advanced ML models for network traffic analysis

Data used: Real network traffic data

Potential Impact

Cybersecurity firms, network infrastructure providers, businesses with significant online presence

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