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POSTER: Performance Characterization of Binarized Neural Networks in Traffic Fingerprinting

Published: 10 July 2023 Publication History

Abstract

Traffic fingerprinting allows making inferences about encrypted traffic flows through passive observation. They have been used for tasks such as network performance management and analytics and in attacker settings such as censorship and surveillance. A key challenge when implementing traffic fingerprinting in real-time settings is how the state-of-the-art traffic fingerprint models can be ported into programmable in-network computing devices with limited computing resources. Towards this, in this work, we characterize the performance of binarized traffic fingerprinting neural networks that are efficient and well-suited for in-network computing devices and propose a new data encoding method that is better suited for network traffic. Overall, we show that the proposed binary neural network with first-layer binarization and last-layer quantization reduces the performance requirement of hardware equipment while retaining the accuracies of those models of binary datasets over 70%. Furthermore, when combined with our proposed encoding algorithm, accuracies of binarized models of numeric datasets show further improvements to achieve over 65% accuracy.

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  1. POSTER: Performance Characterization of Binarized Neural Networks in Traffic Fingerprinting

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      cover image ACM Conferences
      ASIA CCS '23: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
      July 2023
      1066 pages
      ISBN:9798400700989
      DOI:10.1145/3579856
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      Published: 10 July 2023

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      1. datasets
      2. gaze detection
      3. neural networks
      4. text tagging

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