Network traffic classification underpins network security and management but faces growing challenges from pervasive encryption and evolving protocols. Existing representations involve clear trade-offs: hand-crafted flow statistics are efficient but lossy, raw-bit encodings can be accurate but costly, and pre-trained embeddings support transfer but often flatten the protocol stack and entangle cross-layer signals. We observe substantial redundancy both across and within network layers, which existing paradigms do not explicitly address, wasting capacity and encouraging shortcut learning that degrades generalization. To address this, we propose PACC, a redundancy-aware cross-layer representation framework. PACC models protocol layers as multiview inputs and learns faithful, compact projections factorized into shared (cross-layer) and private (layer-specific) components. It incorporates an information-preserving sparse segment representation to reduce structural repetition and supervised intra-layer filtering to suppress task-irrelevant semantic redundancy. A behavior-guided shared/private decomposition aligns cross-layer consensus while retaining layer-specific information, supported by information-theoretic analysis. Across encrypted application classification, IoT device identification, benign/malicious traffic classification, and Tor traffic detection, PACC achieves the best overall performance. On encrypted traffic, it achieves an F1 score of 0.9685, substantially outperforming existing baselines while using approximately 15x fewer computes and less GPU memory than the latest pre-trained traffic model.

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