Generative AI for Network Attack Simulation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P109Keywords:
Generative Artificial Intelligence (Generative AI), Network Attack Simulation, Cybersecurity Simulation, AI-driven Cyber Attack Modeling, Adversarial AIAbstract
Generative Artificial Intelligence (AI) holds remarkable possibilities in a host of applications, such as cybersecurity and various other fields. This paper presents and examines the application of Generative AI for the Simulation of Network attacks. It highlights its key characteristics, areas of cyber security and data science, challenges, and opportunities in addressing the problem of cybercrimes. The research presented in this paper explores the use of Generative AI techniques, such as GANs, to simulate complex cyberattacks with rigor and reliability, thereby spurring the authentication of sophisticated phishing algorithms and bolstering the fortification of sophisticated cybersecurity systems. This study aims to evaluate the feasibility of using AI to conduct simulated network attacks compared to traditional methods. It is important to note that this research was conducted authentically and was done entirely through the care, process, and support of the designated University. In addition, the organizations can foresee the defects within their network and respond much quickly by simulating realistic attack scenarios through patented technologies (such as Generative AI). Subsequently, it proposes that research in this area is also essential to continually assist in innovating responsibly and to address potential risks associated with increasing autonomy of AI systems, e.g., risks of AI systems having a security exploit with button content set as skip and the attribute set as. Additionally, adopting machine learning approaches for Network Attack Simulation would offer more reliable insights into predicting adversary movements, thereby enabling the design of security measures that can potentially prevent critical states in linear time. At the same time, a future emerged where we barely understand the generative adversarial network. In that future, we will need to identify them by leveraging our knowledge of Generative AI technologies and capabilities
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