Speaker
Description
Recent advances in artificial intelligence have transformed the landscape of cybersecurity research, particularly through intelligent agent-based simulations and large language models (LLMs). Traditional cybersecurity datasets are often static and outdated, limiting the capacity to train adaptive AI systems. To address this, we developed a dynamic synthetic simulation framework that transforms static research datasets into interactive, agent-based environments.
Building upon this foundation, our latest work integrates LLM-powered agents capable of generating, understanding, and classifying realistic phishing attacks in a controlled simulation. This presentation introduces the combined framework, where attacker agents simulate social engineering campaigns, and defender agents, which are supported by either classical machine learning or LLMs, analyze and detect phishing content. The integration of LLMs into the simulation framework opens new directions for adaptive cyber defense, human-in-the-loop training, and proactive threat intelligence.
