Confidence Driven Classification of Application Types in the Presence of Background Network

2025-08-05

AI Summary

To address inaccurate application-type classification caused by ubiquitous background traffic—such as advertisements and trackers—in network flows, this paper proposes a confidence-aware deep learning method integrated with Gaussian Mixture Models (GMM). By explicitly modeling probabilistic distributions in the feature space, the approach quantifies classification uncertainty, thereby distinguishing application-specific traffic from heterogeneous background traffic. Crucially, it is the first to explicitly model and suppress background-induced ambiguity in application traffic classification, preventing low-confidence background samples from being misclassified as specific applications. Experiments on real-world network traffic demonstrate that the method significantly reduces background traffic misclassification rates, improves overall classification accuracy and robustness, and provides a reliable uncertainty-aware framework for practical deployment of traffic identification systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Applications
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Accurately classifying the application types of network traffic using deep learning models has recently gained popularity. However, we find that these classifiers do not perform well on real-world traffic data due to the presence of non-application-specific generic background traffic originating from advertisements, analytics, shared APIs, and trackers. Unfortunately, state-of-the-art application classifiers overlook such traffic in curated datasets and only classify relevant application traffic. To address this issue, when we label and train using an additional class for background traffic, it leads to additional confusion between application and background traffic, as the latter is heterogeneous and encompasses all traffic that is not relevant to the application sessions. To avoid falsely classifying background traffic as one of the relevant application types, a reliable confidence measure is warranted, such that we can refrain from classifying uncertain samples. Therefore, we design a Gaussian Mixture Model-based classification framework that improves the indication of the deep learning classifier's confidence to allow more reliable classification.

Problem

• Classifying network traffic application types accurately with background interference
• Addressing confusion between application and heterogeneous background traffic
• Improving confidence measures for reliable uncertain sample classification

Innovation

• Uses Gaussian Mixture Model for confidence
• Adds background traffic class in training
• Improves deep learning classifier reliability