Insight into Insiders and IT: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures
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Abstract (arXiv)
Insider threats are one of today's most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. Despite several scientific works published in this domain, we argue that the field can benefit from the proposed 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 leveraging existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include: 1) Incidents and datasets, 2) Analysis of attackers, 3) Simulations, and 4) Defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents, which 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: a) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, b) an updated overview on publicly available datasets that can be used to test new detection solutions against other works, c) references of existing case studies and frameworks modeling insiders' behaviors for the purpose of reviewing defense solutions or extending their coverage, and d) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 May 2018.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2022Datavoidant: An AI System for Addressing Political Data Voids on Social Mediacited by 6
- 2022Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysiscited by 5
- 2022Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Networkcited by 1
- 2025DMFI: A Dual-Modality Log Analysis Framework for Insider Threat Detection with LoRA-Tuned Language Modelscited by 1
- 2025DMFI: A Dual-Modality Log Analysis Framework for Insider Threat Detection with LoRA-Tuned Language Models6 Aug 2025
- 2022Datavoidant: An AI System for Addressing Political Data Voids on Social Media24 Oct 2022
- 2022Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network16 Jul 2022
- 2022Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis6 Mar 2022