USING GENERATIVE AI FOR SIMULATING CYBER SECURITY ATTACKS AND DEFENSE MECHANISMS: A NEW APPROACH TO AI-DRIVEN CYBER THREAT MODELING - Hamza Rauf; Syed Imran Hussain Shah; Taib Ali; Hassam Gul; Maria Soomro (Spectrum of Engineering Sciences, 2025)
Awareness of cyber risk is a crucial factor in today’s world, where new forms of threats are potentially dangerous to companies’ IT frameworks. The reaction-based detection, particularly the signature based detection and the rule based systems have their limitations in combating new and advanced attacks because they are deficient in the proactive approach. With threats emerging in the cyber realm in a dynamic way, it is imperative that organizations seek out active and conscious defenses. This paper aims at bringing forward the possibility of using Generative Artificial Intelligence (AI) in the process of modeling cyberattacks and defense strategies: a new approach to Defending AI Cyber Threats. Generative AI models such as GAN and VAE are used to generate realistic attack scenarios that are useful in assessing and improving security systems. These allow the emulation of new scenarios that have not yet been experienced in cyberspace that, in turn, can be used to assess defence mechanisms. This is why this approach is popular in cybersecurity research since it can represent such threats as APTs and other types of threats that may appear over time while advancing through the specific attack life cycle. Through leveraging generative AI for threat modeling, organizations start shifting from a reactive approach to threat modeling, with security features matching up with the advancing complexity of threats.