Cyber attacks on critical cyber systems are not decreasing in frequency or complexity. Aggressors choose the time and place of these engagements; protectors must identify, research and develop defensive techniques that provide an asymmetric advantage. A static, data-driven, preventative, automated defense is a losing strategy; an effective defense must be dynamic, behavioral, responsive and capitalize on a human in the loop. We propose human and machine performed linkography to detect, correlate, attribute and predict attacker behavior and present a moving, deceptive target. Recently, our team generated a technology transfer strategy for linkography based cyber security, proposed algorithms to extract and refine linkograph ontologies and subsessionize our input stream and completed our previous related machine learning work. Linkography has been in the literature for decades, and our investigation indicates it is an open, fertile topic for basic and applied cyber security research.