Performance Evaluation of k-means using CPU and GPU with oneAPI and OpenMP for Network Intrusion Detection - Costa, Laura Caetano; Frassi, Luiz Fernando Antunes da Silva; Freitas, Henrique Cota de (Anais Estendidos do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD); 2025: Anais Estendidos do XXVI Simpósio em Sistemas Computacionais de Alto Desempenho, 2025)
This article describes the application of the k-means algorithm for network intrusion detection, evaluating both sequential and parallel CPU and GPU versions using oneAPI and OpenMP. Based on a network intrusion dataset (CIC-IDS2017), which contains millions of instances of modern attacks, the experiments showed that the parallel versions achieved significant performance gains over the sequential version. The good scalability of the CPU solutions and GPU acceleration stand out, with oneAPI performing slightly better than OpenMP. The results reinforce that parallelization is essential for applying k-means in contexts where the data volume is large.