Predictive Defence Against Evolving Cyber Threats Using Generative AI

The growing dependence on online services, cloud computing, and interconnected systems has intensified the probability of cyberattacks and they are more common, dynamic, and complex. Traditional cyber defence tools, which are mostly based on predefined rules and known signatures of attack, find it hard to identify new and zero-day threats. In order to mitigate this shortcoming, this paper puts forward an active structure of cyber-attack prediction by using generative artificial intelligence (AI). 

The system uses traffic data on the network, pattern of user behaviour, and logs on system activity to detect any anomaly that can be a sign of malicious intent. Generative AI methods are used to generate realistic attack examples, which augment training data and improve the detection of attacks that have not been seen before. It is designed to combine a Python-based AI engine to predictive model, secure backend on Node.js and MongoDB to manage data efficiently and a React.js dashboard to provide real-time visualization and notifications. 

The proposed solution enhances the accuracy of detection and minimizes false positives and enhances proactive defences through the use of predictive analytics coupled with generative intelligence. The study will help in the development of intelligent cybersecurity that can predict and reduce the emerging cyber threats and create a more secure and resilient cyber ecosystem.