Cognitive solutions to surveillance in security (CSSS)

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Surveillance in public security and cyber-defence is essential to protecting the country's critical infrastructure and public places against the threat of terrorism, civil unrest, criminal activity, and cyber-attacks. Reliance on human operators to monitor multiple screens and sources of information in real-time has and will lead to undetected incidents, thus increasing the threat to local and national security. Despite advances in surveillance equipment, operators are still faced with many cognitive challenges (e.g., information overload, multitasking, background sound distraction, interruptions, and fatigue) which place stresses on the operator's cognitive system and reduce efficiency. Hence, the development of user-centered adaptive, intelligent systems becomes critical to ensure optimal surveillance work. In this project we adopt an interdisciplinary approach by combining system engineering with cognitive neuroscience to ensure a thorough, empirically-based characterisation of the problem space and then test innovative solutions to improve security and cyber-defence surveillance work. Using well-established cognitive paradigms we seek to establish weak functional points and thus identify areas that need support. To do so, we will create controlled experiments using multi-screen surveillance immersive environments of dynamically evolving crowd scenes or cyber-intrusions alerts, which provide an optimal compromise between external realism and empirical control. The initial empirical work will help develop a set of criteria that will provide sensitive diagnostic metrics with which to assess the ability of different systems to support performance, which in turn will be used to develop and test prototypes of decision support systems and innovative training procedures.