A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intelligence and Anomaly Detection

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM's ability to capture temporal dependencies in sequential data and GAN's strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications.