Performance Evaluation of Convolutional Neural Networks with oneAPI and OpenMP for Network Intrusion Detection
Resumo
The accelerated evolution in the Machine Learning (ML) field has made the use of hardware solutions indispensable for training and inferring models. This article presents the performance evaluation of three implementations of a Convolutional Neural Network: a sequential version and two parallel versions, one using Intel oneAPI and the other with OpenMP, all running on the CPU. The comparative analysis is based on a network intrusion dataset (NSL-KDD), focusing on speedup and scalability. It shows that the implementation using Intel® oneAPI achieves the best performance compared to the sequential and OpenMP versions.Referências
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Codeplay Software Ltd. / Intel. oneAPI for NVIDIA® GPUs. [link]. Accessed in August 20, 2025.
L. Dhanabal and S. Shantharajah. A study on nsl-kdd dataset for intrusion detection system based on classification algorithms. International journal of advanced research in computer and communication engineering, 4(6):446–452, 2015.
L. Ioannou and S. A. Fahmy. Network intrusion detection using neural networks on fpga socs. In 2019 29th International Conference on Field Programmable Logic and Applications (FPL), pages 232–238, 2019. DOI: 10.1109/FPL.2019.00043.
Khronos Group. Sycl overview. [link]. Accessed in August 21, 2025.
I. Kuon, R. Tessier, J. Rose, et al. Fpga architecture: Survey and challenges. Foundations and Trends® in Electronic Design Automation, 2(2):135–253, 2008.
Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 2022.
L. A. Maciel, M. A. Souza, and H. C. Freitas. Energy-efficient cpu+fpga-based cnn architecture for intrusion detection systems. IEEE Consumer Electronics Magazine, 13 (4):65–72, 2024. DOI: 10.1109/MCE.2023.3283730.
R. Peng, W. Li, T. Yang, and K. Huafeng. An internet of vehicles intrusion detection system based on a convolutional neural network. In 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom), pages 1595–1599, 2019. DOI: 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00234.
M.-C. Popescu, V. E. Balas, L. Perescu-Popescu, and N. Mastorakis. Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7): 579–588, 2009.
E. Silva, F. Soares, W. Júnio de Souza, and H. Freitas. A systematic mapping of autonomous vehicle prototypes: Trends and opportunities. IEEE Transactions on Intelligent Vehicles, 9(11):6777–6802, 2024. DOI: 10.1109/TIV.2024.3387394.
E. M. R. Silva and H. C. de Freitas. Task scheduling for autonomous vehicles with heterogeneous processing via integration of the carla simulator and starpu runtime. In 2025 IEEE International Conference on Consumer Electronics (ICCE), pages 1–6, 2025. DOI: 10.1109/ICCE63647.2025.10929988.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani. A detailed analysis of the kdd cup 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pages 1–6, 2009. DOI: 10.1109/CISDA.2009.5356528.
Y.-C. Wang, Y.-C. Houng, H.-X. Chen, and S.-M. Tseng. Network anomaly intrusion detection based on deep learning approach. Sensors, 23(4), 2023. ISSN 1424-8220. DOI: 10.3390/s23042171. URL [link].
L. A. Álvarez Almeida and J. Carlos Martinez Santos. Evaluating features selection on nsl-kdd data-set to train a support vector machine-based intrusion detection system. In 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI), pages 1–5, 2019. DOI: 10.1109/ColCACI.2019.8781803.
Publicado
28/10/2025
Como Citar
OLIVEIRA, Rafael Rodrigues; LEÃO, Thiago Augusto Demeto; FREITAS, Henrique Cota de.
Performance Evaluation of Convolutional Neural Networks with oneAPI and OpenMP for Network Intrusion Detection. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 26. , 2025, Bonito/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 81-88.
DOI: https://doi.org/10.5753/sscad_estendido.2025.16684.
