A Generative AI-Driven Information System for Behavioral Detection of Zero- Day Cyber Attacks

Authors

  • Abdalilah Alhalangy

Keywords:

Information Systems Security, Anomaly Detection, Artificial Intelligence, Behavioral Modeling, Zero-Day Attacks

Abstract

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.

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Published

2025-09-20

How to Cite

Abdalilah Alhalangy. (2025). A Generative AI-Driven Information System for Behavioral Detection of Zero- Day Cyber Attacks. Utilitas Mathematica, 122(2), 1194–1210. Retrieved from https://utilitasmathematica.com/index.php/Index/article/view/2831

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