Visible to the public Modeling insider threat types in cyber organizations

TitleModeling insider threat types in cyber organizations
Publication TypeConference Paper
Year of Publication2017
AuthorsSantos, E. E., Santos, E., Korah, J., Thompson, J. E., Murugappan, V., Subramanian, S., Zhao, Yan
Conference Name2017 IEEE International Symposium on Technologies for Homeland Security (HST)
ISBN Number978-1-5090-6356-7
Keywordsawareness, Bayes methods, Bayesian knowledge bases (BKBs), behavioral modeling, business data processing, Collaboration, computational model, Computational modeling, cultural differences, cyber organizations, cyber security, Human Behavior, human factors, insider threat, insider threat types, insider threats, insider traits, knowledge based systems, manipulation, Metrics, Organizations, policy-based governance, predictability, Predictive models, pubcrawl, Random variables, Resiliency, security of data, social modeling, susceptibility, Trust
Abstract

Insider threats can cause immense damage to organizations of different types, including government, corporate, and non-profit organizations. Being an insider, however, does not necessarily equate to being a threat. Effectively identifying valid threats, and assessing the type of threat an insider presents, remain difficult challenges. In this work, we propose a novel breakdown of eight insider threat types, identified by using three insider traits: predictability, susceptibility, and awareness. In addition to presenting this framework for insider threat types, we implement a computational model to demonstrate the viability of our framework with synthetic scenarios devised after reviewing real world insider threat case studies. The results yield useful insights into how further investigation might proceed to reveal how best to gauge predictability, susceptibility, and awareness, and precisely how they relate to the eight insider types.

URLhttps://ieeexplore.ieee.org/document/7943445
DOI10.1109/THS.2017.7943445
Citation Keysantos_modeling_2017