  Insider threats is the most concerned cybersecurity problem which is poorly
addressed by widely used security solutions. Despite the fact that there have
been several scientific publications in this area, but from our innovative
study classification and structural taxonomy proposals, we argue to provide the
more information about insider threats and defense measures used to counter
them. While adopting the current grounded theory method for a thorough
literature evaluation, our categorization's goal is to organize knowledge in
insider threat research. Along with an analysis of major recent studies on
detecting insider threats, the major goal of the study is to develop a
classification of current types of insiders, levels of access, motivations
behind it, insider profiling, security properties, and methods they use to
attack. This includes use of machine learning algorithm, behavior analysis,
methods of detection and evaluation. Moreover, actual incidents related to
insider attacks have also been analyzed.
