FATPaSE: Proposing a Computationally Intelligent Framework for Automated Target Profiling and Social Engineering

Social engineering is recurrently listed as a prime threat according to major cyber security agencies. Over the course of the last decades, various sources expressed serious concerns about the potential impact of artificial intelligence on automating criminal cyber activities. With the recent developments and skyrocketing popularity of large language models, this threat forecast drastically worsened within an overwhelmingly short time period. In this paper, we propose the framework FATPaSE which pursues the goal of maximizing automation of digital profiling and social engineering aided by computational intelligence techniques. We discuss the design of the framework in detail, argue for possible technological choices to realize specific parts and elaborate on already existing proof of concept work. The interconnection of all components should enable a working, fully automated kill chain. During the subsequent design science oriented implementation, the achievable degree of automation will be intensely studied, resulting in novel, innovative research artifacts. Our progress shall be directly validated in the field based on a realistic, industrial test setup. We will continuously publish our results during the development process and expect to gain deepened insights on the potentials of the idea to contribute to the toolbox of cyber security experts.