Abstract

Expert systems possess human-like expertise for data analysis as well as for decision-making. These systems are suitable when a high level of uncertainty exists. In expert systems, various encryption techniques, such as classical and quantum encryption, protect sensitive information. In these systems, Artificial Intelligence (AI) is used to analyze the data at runtime and to detect unauthorized users in the early stage, especially for tracking online harms. These systems are not entirely secure because the encryption techniques have loopholes, such as the algorithm’s short life expectancy and less computational power. An unauthorized user destroys the precious data and the system and might access these loopholes. As the confidentiality and integrity of expert systems are threatened by intrusions and real-time attacks related to privacy and cybersecurity, there is a need to propose novel methodologies to predict future attacks and identify new threat patterns. To analyze the intruder's behavior and overcome the encryption weaknesses, this paper presents an Artificial General Intelligence-based Rational Behavior Detection Agent (AGI-RBDA). The proposed system possesses human-like rationality for protecting the information like a human mind. It is exposed that the human mind does not apply any encryption technique; instead, it uses various cognitive correlates such as intention, perception, motivation, emotions, and implicit and explicit knowledge for the secrecy of sensitive information. Ultimately, the behavior of different cognitive correlates is exposed and stimulated.