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<p>Smart homes are becoming increasingly more complex and difficult to defend. Expanding Internet of Things (IoT) devices has reshaped socio-technical interactions within smart homes, yet security remains a secondary concern. In addition, users have been shown to lack awareness of potential vulnerabilities, leaving smart homes susceptible to attacks. This paper explores how humans can interact with conversational agents (CAs) to improve their awareness and detection of threats in smart homes. Utilising Endsley’s situational awareness model, this research examines how users <i>perceive</i>, <i>comprehend</i>, and <i>project</i> knowledge of their surroundings to identify security threats when interacting with CAs through a multimodal framework. A mixed-methods study combining quantitative pre-test/post-test analysis with qualitative evaluations revealed that CAs significantly enhanced threat detection accuracy, efficiency, and user confidence across all dimensions of situational awareness when using a multi-modal approach.</p>

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Threat detection in smart homes: A sociotechnical multimodal conversational approach for improved cyber situational awareness

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月11日

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  • Christopher D. McDermott,
  • Mathew Nicho

摘要

Smart homes are becoming increasingly more complex and difficult to defend. Expanding Internet of Things (IoT) devices has reshaped socio-technical interactions within smart homes, yet security remains a secondary concern. In addition, users have been shown to lack awareness of potential vulnerabilities, leaving smart homes susceptible to attacks. This paper explores how humans can interact with conversational agents (CAs) to improve their awareness and detection of threats in smart homes. Utilising Endsley’s situational awareness model, this research examines how users perceive, comprehend, and project knowledge of their surroundings to identify security threats when interacting with CAs through a multimodal framework. A mixed-methods study combining quantitative pre-test/post-test analysis with qualitative evaluations revealed that CAs significantly enhanced threat detection accuracy, efficiency, and user confidence across all dimensions of situational awareness when using a multi-modal approach.