Insight Into Insiders and ITreview
Аннотация: Insider threats are one of today’s most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. In this work, we propose structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research while using an existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include incidents and datasets, analysis of incidents, simulations, and defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents that is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers’ efforts in the domain of insider threat because it provides (1) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, (2) an overview on publicly available datasets that can be used to test new detection solutions against other works, (3) references of existing case studies and frameworks modeling insiders’ behaviors for the purpose of reviewing defense solutions or extending their coverage, and (4) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
Год издания: 2019
Издательство: Association for Computing Machinery
Источник: ACM Computing Surveys
Ключевые слова: Information and Cyber Security, Network Security and Intrusion Detection, Advanced Malware Detection Techniques
Другие ссылки: ACM Computing Surveys (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
DataCite API (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
DataCite API (HTML)
Открытый доступ: green
Том: 52
Выпуск: 2
Страницы: 1–40