A NEURAL NETWORK APPROACH FOR CYBER THREAT DETECTION VIA EVENT PROFILING

The increasing sophistication of cyber threats demands more advanced detection systems capable of identifying novel attacks while maintaining efficiency and interpretability. This paper presents a hybrid Transformer-LSTM neural network for real-time cyber threat detection through security event profiling. Unlike existing approaches, our model combines the long-range dependency capture of Transformers with the temporal pattern recognition of LSTMs, enabling more accurate identification of complex attack sequences. Additionally, we integrate a self-supervised learning mechanism that allows the model to dynamically adapt to emerging threats without requiring retraining on fixed datasets. Evaluated on the CIC-IDS2017 dataset, our approach achieves 98.7% precision, 97.5% recall, and a 98.1% F1-score, outperforming state-of-the-art methods such as DeepLog, LSTM, and GNN-based detectors. The model also demonstrates computational efficiency, with a 30% reduction in training time compared to existing architectures. Beyond performance improvements, our framework incorporates attention-based explainability, providing security analysts with interpretable insights into detection decisions. These advancements address critical gaps in adaptability, scalability, and transparency for neural network-based threat detection systems. Our results highlight the potential of hybrid deep learning architectures in building more robust and dynamic cybersecurity defenses.