Artificial general intelligence-based rational behavior detection using cognitive correlates for tracking online harms

Expert systems possess human-like expertise for data analyzing as well as for decision-making. These systems are suitable in a situation, where a high level of uncertainty exists. In expert systems, for protecting sensitive information, various encryption techniques such as classical encryption and quantum encryption are used. 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 completely secured, because the encryption techniques have some loopholes such as the algorithm’s short life expectancy and less computation power. An unauthorized user destroys the precious data, as well as the system, might access these loopholes. As the confidentiality and integrity of expert systems are threatened by intrusions and real-time attacks related to privacy and cyber-security, there is a need for proposing novel methodologies to predict future attacks and identify new threat patterns. To analyze the behavior of the intruder 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 kind of encryption technique; instead, it used various cognitive correlates such as intention, perception, motivation, emotions, and implicit and explicit knowledge for the secrecy of sensitive information. In the end, the behavior of different cognitive correlates is exposed and stimulated.