Serious games use entertainment principles, creativity, and technology to meet government or corporate training objectives, but these principles alone will not guarantee that the intended learning will occur. To be effective, serious games must incorporate sound cognitive, learning, and pedagogical principles into their design and structure. In this paper, we review cognitive principles that can be applied to improve the training effectiveness in serious games and we describe a process we used to design improvements for an existing game-based training application in the domain of cyber security education.

human factors and social/behavioral science challenges through modeling and advanced engineering/computing approaches. This research focuses on the intelligence domain, including human behavior modeling with application to identifying/predicting malicious insider cyber activities, modeling socio-cultural factors as predictors of terrorist activities, and human information interaction concepts for enhancing intelligence analysis decision making. Dr. Greitzer's research interests also include evaluation methods and metrics for assessing effectiveness of decision aids, analysis methods and displays. Ryan Hohimer is a Senior Research Scientist at PNNL. His research interests include knowledge representation and reasoning, probabilistic reasoning, semantic computing, cognitive modeling, image analysis, data management, and data acquisition and analysis. He is currently serving as Principal Investigator of a Laboratory-directed Research and Development project that has designed and developed the CHAMPION reasoner. AbstractThe insider threat ranks among the most pressing cyber-security challenges that threaten government and industry information infrastructures. To date, no systematic methods have been developed that provide a complete and effective approach to prevent data leakage, espionage, and sabotage. Current practice is forensic in nature, relegating to the analyst the bulk of the responsibility to monitor, analyze, and correlate an overwhelming amount of data. We describe a predictive modeling framework that integrates a diverse set of data sources from the cyber domain, as well as inferred psychological/motivational factors that may underlie malicious insider exploits. This comprehensive threat assessment approach provides automated support for the detection of high-risk behavioral "triggers" to help focus the analyst's attention and inform the analysis. Designed to be domain-independent, the system may be applied to many different threat and warning analysis/sense-making problems.

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Abstract-Organizations often suffer harm from individuals who bear no malice against them but whose actions unintentionally expose the organizations to risk-the unintentional insider threat (UIT). In this paper we examine UIT cases that derive from social engineering exploits. We report on our efforts to collect and analyze data from UIT social engineering incidents to identify possible behavioral and technical patterns and to inform future research and development of UIT mitigation strategies.

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