Generative Artificial Intelligence

The rapid growth of internet services and digital communication has increased the risk of cyberattacks, making early prediction and detection an important aspect of cybersecurity. Traditional machine learning techniques such as Decision Trees, Support Vector Machines, and Random Forests have been widely applied to analyze network data and identify possible attack patterns. Although these methods can detect known threats with reasonable accuracy, they often face limitations when dealing with new and evolving cyber threats. Recent developments in generative artificial intelligence provide advanced capabilities for learning complex data patterns and identifying unusual behaviors in network traffic. Generative AI models can assist in predicting potential cyberattacks, improving threat detection accuracy, and supporting automated security responses. This study discusses the transition from traditional machine learning approaches to generative artificial intelligence for cyberattack prediction and highlights how these advanced techniques can strengthen modern cybersecurity systems.