CYBER THREAT FORECASTING: THE TRANSITION FROM TRADITIONAL ML TO GENERATIVE AI APPROACHES
TL;DR
This paper presents a comprehensive framework for cyber threat forecasting by integrating traditional machine learning techniques with advanced Generative AI models and demonstrates that Generative AI significantly improves cyber threat prediction accuracy, reduces false-positive rates, enhances adaptive learning, and supports real-time security decision-making.
Abstract
The increasing sophistication of cyber threats has created significant challenges for organizations in protecting digital infrastructures, sensitive information, and critical services. Traditional cybersecurity solutions based on signature matching and rule-based systems are often unable to detect emerging attack patterns, zero-day vulnerabilities, and advanced persistent threats in dynamic network environments. Machine Learning (ML) has improved cyber threat detection by enabling intelligent classification of malicious activities using historical security data. However, conventional ML models often require extensive feature engineering and exhibit limited adaptability to evolving attack behaviors. Recent advances in Generative Artificial Intelligence (Generative AI) have transformed cybersecurity by enabling intelligent threat forecasting, automated attack simulation, synthetic data generation, adaptive anomaly detection, and proactive security analysis. This paper presents a comprehensive framework for cyber threat forecasting by integrating traditional machine learning techniques with advanced Generative AI models. The proposed framework utilizes network traffic analysis, system logs, user behavior analytics, threat intelligence feeds, and security event data to predict future cyber threats. Comparative analysis is performed using conventional machine learning algorithms and Generative AI approaches to evaluate forecasting accuracy, prediction capability, and computational efficiency. Experimental results demonstrate that Generative AI significantly improves cyber threat prediction accuracy, reduces false-positive rates, enhances adaptive learning, and supports real-time security decision-making. The proposed framework contributes to the development of intelligent cybersecurity systems capable of proactively forecasting cyber threats and strengthening organizational resilience against rapidly evolving cyberattacks.