Towards An Adaptable System-based Classification Design for Cyber Identity

As cybercrime activity continues to increase with significant data growth and the Internet of Things (IoT's), this research introduces a new proactive methodically designed approach vs. current reactive and specialized methods. A novel holistic identity classification scheme and information architecture is proposed. This approach has an adaptive, common cybernetic trait design to support a changing technological landscape and human behavior. Common cyber identity base trait dimensions for context, physical, cyber, and human aspects allow for systematic analysis of temporal evidence to help resolve a physical person's identity in a cybercrime. This research platform supports both broad and targeted identity analytics utilizing advanced machine learning methods with mixed media visualizations to facilitate Cyber Situational Awareness (SA). Early PhD experimentation with real-world use cases shows promise with regard to providing salient attributes and patterns of cyber activity that are unique to a person.