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Gersh, John

Publications and source records attributed to Gersh, John.

Effects of Gain/Loss Framing in Cyber Defense Decision-Making

Cyber defense requires decision making under uncertainty. Yet this critical area has not been a strong focus of research in judgment and decision-making. Future defense systems, which will rely on software-defined networks and may employ ‘moving target’ defenses, will increasingly automate lower level detection and analysis, but will still require humans in the loop for higher level judgment. We studied the decision making process and outcomes of 17 experienced network defense professionals who worked through a set of realistic network defense scenarios. We manipulated gain versus loss framing in a cyber defense scenario, and found significant effects in one of two focal problems. Defenders that began with a network already in quarantine (gain framing) used a quarantine system more than those that did not (loss framing). We also found some difference in perceived workload and efficacy. Alternate explanations of these findings and implications for network defense are discussed.

cyber security↗

Modeling in Big Data Environments

Human-Centered Big Data Research (HCBDR) is an area of work that focuses on the methodologies and research areas focused on understanding how humans interact with “big data”. In the context of this paper, we refer to “big data” in a holistic sense, including most (if not all) the dimensions defining the term, such as complexity, variety, velocity, veracity, etc. Simply put, big data requires us as researchers of to question and reconsider existing approaches, with the opportunity to illuminate new kinds of insights that were traditionally out of reach to humans. The purpose of this article is to summarize the discussions and ideas about the role of models in HCBDR at a recent workshop. Models, within the context of this paper, include both computational and conceptual mental models. As such, the discussions summarized in this article seek to understand the connection between these two categories of models.

big data↗