TrafficHD: Efficient Hyperdimensional Computing for Real-Time Network Traffic Analytics
Article No.: 271, Pages 1 - 6
Abstract
With the evolution of network infrastructure, the pattern of network traffic becomes unprecedentedly complex. Conventional machine learning algorithms struggle to cope with the high-dimensional data and real-time processing speeds required in such complex networks. Fortunately, Hyperdimensional Computing (HDC), which is power-efficient and supports parallel processing, provides a potential solution to this challenge. In this paper, we present TrafficHD, a novel classification framework that leverages HDC to analyze network traffic in real-time. By transforming network traffic features into high-dimensional binary vectors, TrafficHD enables the rapid execution of recognition tasks within the constraints of real-time systems. Extensive evaluations on a wide range of network tasks show that TrafficHD is 30.57× and 98.32× faster than state-of-the-art (SOTA) machine learning and HDC algorithms while providing 3× higher robustness to network noise.
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- TrafficHD: Efficient Hyperdimensional Computing for Real-Time Network Traffic Analytics
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Published: 07 November 2024
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- Research-article
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- National Key Research and Development Program of China
- Shanghai Pujiang Program
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DAC '24
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