RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices
Traffic classification (TC) is crucial to secure Internet of Things (IoT) networks, whose edge nodes often operate under privacy, bandwidth, and energy constraints. Yet, encrypted payloads and limited computing power make accurate, real-time TC a challenging task. Existing learning-based TC approaches often fix the input configuration a priori, even though it directly influences both predictive performance and computational cost. This paper presents RepTC, a representation-aware hardware-constrained strategy that addresses session-level TC through joint model and input optimization. The method co-optimizes network architecture, session length, and header preprocessing; this enables joint ...
| Authors | Adel Chehade, Edoardo Ragusa, Paolo Gastaldo et al. |
|---|---|
| Primary category | cs.NI |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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