Smart Networks Traffic Analysis and Anomaly Detection using Bidirectional LSTM Networks with CUDA Kernel Utilization - Freitas, Francisco Erialdo Domingos; Rodrigues, Antônio Wendell de Oliveira (Anais da Escola Regional de Alto Desempenho da Região Nordeste (ERAD-NE); 2026: Anais da VII Escola Regional de Alto Desempenho da Região NordesteDO - 10.5753/erad-ne.2026.26676, 2026)
Modern networks, particularly those involving the Internet of Things (IoT), face significant challenges in traffic management, classification, and security. Efficient traffic classification and anomaly detection are critical for protecting these networks against malicious activities. In this work, we propose an approach based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks, leveraging CUDA acceleration in PyTorch to optimize training performance and model accuracy. Our method captures contextual information from both past and future sequences, enhancing detection capabilities. Experimental results on an industrial IoT dataset demonstrate superior accuracy, recall, and F1 score compared to conventional LSTM models, highlighting the potential of the proposed solution for improving security and reliability in smart network environments.