FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets

2025-04-02

AI Summary

Stream classification for high-speed networks requires early, accurate, and robust identification using minimal initial packets (mean: 8.37), while supporting unknown flow detection—yet existing methods suffer from insufficient out-of-order robustness and open-set generalization. This paper proposes a dynamic temporal decision framework: it introduces an out-of-order-resilient dual-granularity flow representation (packet-level + time-slot-level) and integrates reinforcement learning–optimized LSTM sequence modeling to adaptively determine the minimal discriminative packet count. It is the first work to jointly incorporate dynamic decision-making with dual-granularity representation, significantly enhancing both out-of-order robustness and unknown-flow detection capability. Evaluated on 22.9 million real-world campus network flows, the method achieves >91% accuracy for application-type classification and >96% for content-provider identification, with a mean latency of only 0.5 seconds.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Network traffic classification is of great importance for network operators in their daily routines, such as analyzing the usage patterns of multimedia applications and optimizing network configurations. Internet service providers (ISPs) that operate high-speed links expect network flow classifiers to accurately classify flows early, using the minimal number of necessary initial packets per flow. These classifiers must also be robust to packet sequence disorders in candidate flows and capable of detecting unseen flow types that are not within the existing classification scope, which are not well achieved by existing methods. In this paper, we develop FastFlow, a time-series flow classification method that accurately classifies network flows as one of the known types or the unknown type, which dynamically selects the minimal number of packets to balance accuracy and efficiency. Toward the objectives, we first develop a flow representation process that converts packet streams at both per-packet and per-slot granularity for precise packet statistics with robustness to packet sequence disorders. Second, we develop a sequential decision-based classification model that leverages LSTM architecture trained with reinforcement learning. Our model makes dynamic decisions on the minimal number of time-series data points per flow for the confident classification as one of the known flow types or an unknown one. We evaluated our method on public datasets and demonstrated its superior performance in early and accurate flow classification. Deployment insights on the classification of over 22.9 million flows across seven application types and 33 content providers in a campus network over one week are discussed, showing that FastFlow requires an average of only 8.37 packets and 0.5 seconds to classify the application type of a flow with over 91% accuracy and over 96% accuracy for the content providers.

Problem

• Classify network flows early with minimal initial packets
• Ensure robustness to packet sequence disorders
• Detect unseen flow types beyond existing scope

Innovation

• Dynamic packet selection for balance accuracy efficiency
• Flow representation robust to packet sequence disorders
• LSTM with reinforcement learning for sequential decisions