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Intrusion Detection in Smart Homes: A pretest-posttest study of learning modalities for improved Cyber Situational Awareness
  • Christopher D. McDermott ,
  • Mathew Nicho ,
  • John P. Isaacs
Christopher D. McDermott
Robert Gordon University

Corresponding Author:[email protected]

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Mathew Nicho
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John P. Isaacs
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Abstract

Smart homes are becoming increasingly more com- plex and difficult to defend. This paper explores how humans can interact with intelligent virtual agents to improve their awareness and detection of threats in smart homes. Mica Endsley’s situa- tional awareness (SA) model was adopted and used to assess how participants perceive device activity, comprehend this in the context of their environment, and project this knowledge to detect if a threat exists. A pretest-posttest study demonstrated a statistically significant improvement in accuracy and efficiency when detecting threats using the conversational agents. In addi- tion, a qualitative approach was also taken to examine usability in relation to a users preferred learning modality. Participants reported being more confident at detecting threats across the three elements of the SA model when using the conversational agents as part of a multi-modal approach.