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Sparse-selective quantization for real-time cyber threat detection in large-scale networks.

Yongsheng Xie1, Rifeng Wang1, Liliang Dong2

  • 1School of Artificial Intelligence, Guangxi Science & Technology Normal University, Laibin, Guangxi, China.

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This study introduces a novel framework for real-time cyber threat detection, enhancing network security through efficient data analysis and accurate identification of rare attacks. It achieves high performance with low latency for large-scale networks.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Real-time cyber threat detection in large-scale networks faces challenges in balancing computational efficiency and detection accuracy.
  • Existing intrusion detection systems often struggle with high-dimensional network traffic data and identifying rare, severe threats.

Purpose of the Study:

  • To propose a sparse-selective quantization framework for efficient and accurate real-time cyber threat detection.
  • To enable analysis of high-dimensional network traffic data without losing critical attack signatures.
  • To develop a scalable solution compatible with existing infrastructure and suitable for edge deployment.

Main Methods:

  • The framework integrates sparsity-aware feature selection and dynamic precision quantization.
  • A lightweight deep learning classifier with a GRU-attention mechanism is employed for threat classification.
  • Optimization using TensorRT achieves inference latency below one millisecond; implementation with TensorFlow Lite facilitates edge deployment.

Main Results:

  • The proposed method demonstrates significant improvements in both speed and accuracy compared to conventional intrusion detection systems.
  • Effective detection of rare but severe cyber threats is achieved.
  • The framework achieves inference latency below one millisecond, enabling real-time analysis.

Conclusions:

  • The unified approach of sparsity analysis, adaptive quantization, and efficient deep learning offers a cohesive and scalable solution for real-time cyber threat detection.
  • Linking feature-level sparsity patterns to dynamic quantization policies differentiates this work from prior methods.
  • The framework is suitable for edge deployment in resource-constrained environments, advancing the state-of-the-art in network security.