Presentation
TrafficHD: Efficient Hyperdimensional Computing for Real-Time Network Traffic Analytics
DescriptionWith the evolution of network infrastructure, the pattern of network traffic becomes unprecedentedly complex. Conventional machine learning algorithms are struggling 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 achieves 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.
Event Type
Research Manuscript
TimeThursday, June 272:45pm - 3:00pm PDT
Location3002, 3rd Floor
AI
AI/ML Application and Infrastructure