Out-Of-Distribution Is Not Magic: The Clash Between Rejection Rate and Model Success

Recent advancements in Internet protocols, including DNS over HTTPS (DoH) and Encrypted Service Name Indicators (ESNI), are making traditional Deep Packet Inspection (DPI) engines obsolete. Consequently, there is a growing need for next-generation traffic classification using artificial intelligence (AI). While DPI automatically categorizes unknown traffic as 'other,' AI-based models cannot automatically handle unknown or Out-of-Distribution (OOD) traffic. AI models must effectively detect and classify OOD traffic to ensure robustness, reliability, and accuracy in real-world applications; however, current research often fails to address the challenges of OOD detection.In this paper, we evaluate various state-of-the-art OOD detection techniques for internet traffic classification and explore the drawbacks and advantages of using different threshold levels for the model's tolerance for OOD. Our findings reveal that varying rejection rates have distinct effects on OOD techniques, leading to a change in the optimal strategy for achieving dependable and precise detection across diverse OOD scenarios. We demonstrate that adjusting rejection rates from 10% to 30% can significantly improve the True Detection Rate (TDR) by up to 50%, while the False Detection Rate (FDR) may increase by less than 10%. Moreover, we emphasize that rejection-rate-based evaluation is pivotal for next-generation flow classification, promising a substantial reduction in FDR through rigorous methodological assessment.