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Academic researchers currently face a significant gap in validating anomaly detection algorithms due to the lack of real-life insider threat scenarios. It is well recognized that stimulating and developing scenarios is a way to overcome potential risks that could occur in the future. Identifying patterns between possible scenario outcomes is a strategy that acts proactively against insider threat attacks. This research study investigates real-life scenario patterns of insider attacks in different industries. It aims at identifying the attributes that play key roles in detecting anomalies in insider's behavior. To create a real-life scenario insider threat-based model, multiple interviews were conducted with participants across 5 industries in the Gulf Cooperation Council (GCC) countries. The industries examined include technology, communications, utilities, education, and finance. The results show correlations between different identified attributes and critical threat indicators.
Gulf-cooperation-councilAnomaly-detectionInsider-threatsScenario-developmentSecurityUser-behavior-analysis