sesa 13(2): e4

Research Article

Training organizational supervisors to detect and prevent cyber insider threats: two approaches

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  • @ARTICLE{10.4108/trans.sesa.01-06.2013.e4,
        author={Dee H. Andrews  and Jared Freeman and Terence S. Andre and John Feeney and Alan Carlin  and Cali M. Fidopiastis and Patricia Fitzgerald},
        title={Training organizational supervisors to detect and prevent cyber insider threats: two approaches},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={1},
        number={2},
        publisher={ICST},
        journal_a={SESA},
        year={2013},
        month={5},
        keywords={accelerated learning, cognitive principles, cyber insider threat, game-based instruction.},
        doi={10.4108/trans.sesa.01-06.2013.e4}
    }
    
  • Dee H. Andrews
    Jared Freeman
    Terence S. Andre
    John Feeney
    Alan Carlin
    Cali M. Fidopiastis
    Patricia Fitzgerald
    Year: 2013
    Training organizational supervisors to detect and prevent cyber insider threats: two approaches
    SESA
    ICST
    DOI: 10.4108/trans.sesa.01-06.2013.e4
Dee H. Andrews 1,2,*, Jared Freeman3, Terence S. Andre4, John Feeney3, Alan Carlin 3, Cali M. Fidopiastis4, Patricia Fitzgerald2
  • 1: Army Research Institute, 425 E. Melody Lane, Gilbert, Arizona, USA, 85234
  • 2: Formerly with the Air Force Research Laboratory
  • 3: Aptima, Inc.
  • 4: Tier1 Performance Solutions, Inc.
*Contact email: dee.h.andrews@us.army.mil

Abstract

Cyber insider threat is intentional theft from, or sabotage of, a cyber system by someone within the organization. This article explores the use of advanced cognitive and instructional principles to accelerate learning in organizational supervisors to mitigate the cyber threat. It examines the potential advantage of using serious games to engage supervisors. It also posits two systematic instructional approaches for this training challenge – optimal path modelling and a competency-based approach. The paper concludes by discussing challenges of evaluating training for seldom occurring real world phenomena, like detecting a cyber-insider threat.