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This study introduces a platform for collaboratively teaching smart home assistants, effectively detecting and mitigating malicious users who spread inappropriate content. The system enhances user-driven learning while maintaining content integrity in shared AI responses.

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Cybersecurity

Background:

  • Smart home assistants are integral to modern homes, offering control and conversational capabilities.
  • End-user customization of AI responses raises security concerns due to potential misuse by malicious actors.
  • Existing systems lack robust mechanisms for detecting and preventing the spread of inappropriate user-generated content.

Purpose of the Study:

  • To develop a platform enabling collaborative, natural language-based teaching of smart home assistant responses.
  • To implement a collective detection method for identifying malicious users.
  • To leverage detected malicious commands to proactively mitigate future harmful contributions.

Main Methods:

  • Development of a collaborative platform for user-driven AI response training.
  • Implementation of a collective intelligence approach for identifying malicious user behavior.
  • Utilizing learned patterns from malicious inputs to enhance system defenses.

Main Results:

  • Demonstrated effectiveness of the platform in a user study with 192 participants.
  • Successful identification and mitigation of malicious user activities.
  • Improved resilience of the smart home assistant against inappropriate content injection.

Conclusions:

  • The proposed platform effectively addresses the challenge of malicious users in collaborative AI training.
  • Collective detection and adaptive mitigation strategies are crucial for secure user-driven AI systems.
  • This approach enhances the safety and reliability of shared AI learning environments.