INTELLIGENT CYBER THREAT PREDICTION USING MACHINE LEARNING AND GENERATIVE AI MODELS

Abstract

The increasing sophistication and frequency of cyber attacks pose significant threats to modern digital infrastructures, necessitating the development of advanced predictive mechanisms for proactive defense. Traditional cybersecurity approaches primarily rely on signature-based detection and rule-based systems, which are often ineffective against evolving and zero-day attacks. Machine learning (ML) has emerged as a powerful tool for cyber attack prediction by enabling systems to learn patterns from historical data and detect anomalies. However, conventional ML models are limited in handling complex attack patterns and generating adaptive responses. This research explores the evolution of cyber attack prediction techniques, transitioning from traditional machine learning methods to advanced generative artificial intelligence (AI) models. The proposed framework integrates supervised and unsupervised learning techniques with generative models such as Generative Adversarial Networks (GANs) and transformer-based architectures to enhance predictive capabilities. The system leverages network traffic data, system logs, and behavioral analytics to identify potential threats in real time. Generative AI models are employed to simulate attack scenarios, augment training datasets, and improve model robustness against unseen threats. Additionally, the framework incorporates anomaly detection, feature extraction, and threat intelligence integration to provide a comprehensive security solution. Experimental results demonstrate that generative AI-based models outperform traditional ML approaches in terms of accuracy, detection rate, and adaptability to new attack patterns. The system also reduces false positives and improves response time, making it suitable for real-world deployment in enterprise and cloud environments. Furthermore, the integration of explainable AI techniques enhances transparency and trust in the model’s predictions. This research highlights the transformative potential of generative AI in cybersecurity, enabling proactive threat detection and adaptive defense mechanisms.