Leveraging Generative Artificial Intelligence (AI) for cybersecurity: Analyzing diffusion models in detecting and mitigating cyber threats

Due to the increased number of cyber dangers in today's digitally connected world, more advanced and flexible security measures have had to be created. Using generative artificial intelligence (AI), especially diffusion models, to find and stop cyber threats is investigated in this study. A new type of generative models called diffusion models have shown great promise in many areas, including picture creation and natural language processing. The purpose of this study is to look at their use in cybersecurity, especially for finding strange patterns, predicting future threats, and stopping attacks as they happen. The study utilized five scientific databases and a systematic search strategy to identify research articles on PubMed, Google Scholar, Scopus, IEEE, and Science Direct relating to the topic. Furthermore, books, dissertations, master's theses, and conference proceedings were utilized in this study. This study encompassed all publications published until 2024. Through a thorough study of the diffusion model's structure and how it can be applied to cybersecurity issues, we look at how these models can improve current systems for finding threats. Additionally, we talk about their ability to add to datasets by creating fake data, which makes anomaly detection more accurate in cyberattack cases that aren't well represented. Due to their stability and ability to predict, diffusion models are seen as a useful tool for finding complex threats like advanced persistent threats (APTs) and zero-day attacks. Some problems still exist, though, such as the need for a lot of computing power, models that are hard to understand, and the fact that online threats are always changing. This article suggests avenues for future study and talks about how diffusion models might change the way cybersecurity is done.