Resumen
This paper discusses necessary components of a GPU-assisted traffic classification method, which is capable ofmulti-Gbps speeds on commodity hardware. The majority of the traffic classification is pushed to the GPU to offload the CPU, which then may serve other processing intensive tasks, e.g., traffic capture. The paper presents two massively parallelizable algorithms suitable for GPUs. The first one performs signature search using a modification of Zobrist hashing. The second algorithm supports connection pattern-based analysis and aggregation of matches using a parallel-prefix-sum algorithm adapted to GPU.The performance tests of the proposed methods showed that traffic classification is possible up to approximately 6 Gbps with a commodity PC.