In this paper, we present a novel encrypted traffic classification model that operates directly on raw PCAP data without requiring prior assumptions about traffic type. Unlike existing methods, it is generalizable across multiple classification tasks and leverages inter-flow signals—an innovative representation that captures temporal correlations and packet volume distributions across flows. Experimental results show that our model outperforms well-established methods in nearly every classification task and across most datasets, achieving up to 99% accuracy in some cases, demonstrating its robustness and adaptability.