Comparative Analysis of Transformer and LSTM Architectures for Cybersecurity Threat Detection Using Machine Learning

The growing prevalence of advanced persistent threats (APTs), zero-day exploits, and the rapid proliferation of IoT devices have exposed limitations in traditional cybersecurity approaches. In response, this study presents a comparative analysis of deep learning models—specifically Long Short-Term Memory (LSTM) and Transformer-based architectures—for cybersecurity threat classification from textual data. Leveraging a standardized dataset and consistent preprocessing pipeline, both models are evaluated across key performance metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that Transformer models significantly outperform LSTM-based approaches, exhibiting superior capacity to capture long-range dependencies, handle complex threat narratives, and generalize to previously unseen data. These findings offer valuable insights into the practical application of modern deep learning techniques in cybersecurity and provide a foundation for designing more robust and adaptive threat detection systems.