A Generative AI-Driven Information System for Behavioral Detection of Zero- Day Cyber Attacks
Keywords:
Information Systems Security, Anomaly Detection, Artificial Intelligence, Behavioral Modeling, Zero-Day AttacksAbstract
Zero-day attacks are among the most serious cybersecurity threats because they
occur before recognizable signatures are available, rendering traditional detection
methods ineffective. This research aims to develop an intelligent, generative AI
based detection framework capable of simulating and identifying unknown cyber
threats in real time. In this research paper, we present a model that uses generative
transformers to simulate sophisticated attack behaviors based on historical
sequence data and attacker profiles.











