# Generalized Encrypted Traffic Classification Using Inter-Flow Signals

## TL;DR

This paper introduces a new encrypted traffic classification model that uses inter-flow signals to analyze raw PCAP data, achieving high accuracy and generalizability across multiple tasks and datasets.

## Contribution

It proposes a novel inter-flow signal-based approach that operates directly on raw PCAP data, enhancing flexibility and performance over existing methods.

## Key findings

- Achieves up to 99% accuracy in classification tasks
- Outperforms existing methods in most datasets
- Demonstrates robustness and adaptability across tasks

## Abstract

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.