Advanced Cyber Incident Prediction Using Attention-based Temporal Fusion Transformer with Dynamic Optimization
Keywords:
Attention mechanism, Cyber incident prediction, Cybersecurity, Deep learning, Optimization algorithm, Temporal fusion transformerAbstract
Cyberattacks are becoming more sophisticated and frequent, creating major challenges for modern digital infrastructures. Conventional cybersecurity systems mainly depend on reactive defense strategies and static detection techniques, which are often inadequate for identifying emerging and complex threats. To overcome these limitations, this research introduces an optimized temporal fusion transformer (OTFT) framework for the classification and prediction of significant cyber incidents (SCI). The proposed approach combines temporal feature extraction, gated residual learning, recurrent processing, and multi-head attention mechanisms to analyze complex cybersecurity event sequences effectively. Unlike traditional sequential models, the proposed framework can learn both immediate behavioral changes and long-term temporal relationships from multivariate cyber datasets. An adaptive dynamic population control-based polar bear optimization algorithm (DPCPBOA) is further integrated to optimize critical hyperparameters, improve convergence speed, and minimize overfitting during training. The framework was evaluated using benchmark cybersecurity datasets containing large-scale attack records and temporal event information. Experimental analysis demonstrates that the OTFT model achieves superior predictive performance compared with conventional machine learning and deep learning approaches, including LSTM and Seq2Seq architectures. The proposed framework achieved high accuracy, precision, recall, and F1-score while maintaining strong robustness and generalization capability. The overall findings indicate that the proposed model can serve as an intelligent and scalable solution for proactive cyber threat forecasting and advanced cybersecurity management.
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