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Derivation of Physiologic, Neurophysiologic, and Behavioral Indices to Support Real-time Assessment and Augmentation of Team Performance within the Cyber Domain


OBJECTIVE: Development of a solution which provides real-time objective measurement and assessment of team functional states through advanced behavioral, neurophysiological, and physiological signal processing and data modeling. DESCRIPTION: The DoD is currently engaged in full-spectrum cyber operations that place enormous demands on the technological systems and human operators tasked with this mission. As the cyber threat continues to grow, additional task loading will necessitate methods of performance augmentation to ensure mission success. Full spectrum cyber operations can be divided into two main categories: Cyber Network Attack (CNA) and Cyber Network Defense (CND). Cyber Network Defense (CND) refers to the constant monitoring and defense against possible and emerging cyber threats. Conversely, Cyber Network Attack (CNA) is a coordinated effort to achieve operational results that may reside specifically in the Cyber domain or could involve traditional kinetic operations. Regardless of whether an operator or team is faced with CND or CNA tasks, day-to-day operations within the Cyber domain require significant multi-tasking and a keen contextual awareness of the constantly changing environment. Knowledge and understanding of real-time and historical changes in team states are necessary so that appropriate augmentation strategies can be executed in real-time to optimize team performance. Team performance measures include the time to identify cyber opportunities and/or threats and employ exploitation measures to gain an advantage. Studies based out of the Air Force Research Laboratory (AFRL) have indicated that synchronicity in both behavioral and physiological/neurophysiological responses may provide significant benefits and enhancement of individual operator and team performance. [1-4] Research in the Air Traffic Control (ATC) and Remotely Piloted Aircraft (RPA) domains has surrounded the signal processing and modeling of physiological and neurophysiological data in order to support classification of operator cognitive state.[2,3] The major goal of this research is the development of adaptive aiding strategies which will avoid unwanted and potentially catastrophic performance decrements. [4] Similar research is needed within the Cyber domain where operators and teams must be vigilant at all times in order to recognize both emerging threats and provide timely and effective defense against them and identify opportunities for targeted attack. An effective solution would be one which is adaptable within the ever-changing complexity of the Cyber domain. PHASE I: Generate a conceptual framework to demonstrate the initial feasibility of a fully integrated and deployable system capable of real-time assessment and augmentation of team performance within the Cyber domain. Integrate real-time processing and modeling of pertinent physiological, neurophysiological, and behavioral data. PHASE II: Collect data to validate the measurement, assessment, and augmentation capabilities of the system in representative settings. Expand the system"s capabilities to adapt across different cyber-team scenarios, i.e., CNA and CND. Demonstrate real-time assessment and augmentation capabilities of the developed system. Develop appropriate visualization and data play-back options to support use within the cyber-intelligence operational setting. PHASE III: Transition the system within real-world operational environments and demonstrate the functionality, reliability, and usability. Ensure system design is adaptable to changes in the technological and structural hierarchy of the cyber-intelligence domain. REFERENCES: 1. Strang A., Funke G.J., Knott B.A., & Warm J.S. (2011) Physio-behavioral synchronicity as an index of processes supporting team performance Proceedings of the Human Factors and Ergonomics Society Annual Meeting 55, 1447-1451. 2. Wilson, G. F., & Russell, C. A. (2003). Operator functional state classification psychophysiological features in an air traffic control task. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(3), 381-389. 3. Wilson, G. F., & Russell, C. A. (2003). Real-time assessment of mental psychophysiological measures and artificial neural networks. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(4), 635-644. 4. Wilson, G. F., & Russell, C. A. (2007). Performance enhancement in an uninhabited task using psychophysiologically determined adaptive aiding. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49(6), 1005-1018.
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