Keywords:-

Keywords: Error Handing, multi-layer neural network, misclassification, feedback

Article Content:-

Abstract

The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, particularly in access control and error handling. This study developed a Usability Model for Zero Trust IoT Systems, integrating continuous security evaluation with user-centric feedback and adaptive mechanisms to maintain both security and usability. The system incorporated a robust error-handling mechanism, which detected, classified, and responded to errors in real time. Errors were categorized as Critical, User Mistake, or System Error, and appropriate actions Deny, Challenge, or Degrade were automatically generated. The model used features such as user trust level, device type, and previous errors to predict access decisions and ensure consistent policy enforcement. Training was conducted using a multi-layer neural network, with results evaluated through accuracy, loss, confusion matrices, and class distribution graphs. The model achieved a training accuracy of 95% and a validation accuracy of 92%, effectively minimizing misclassification errors. Feature importance analysis revealed that previous errors and user trust level contributed most significantly to decision-making. An interactive interface was developed using HTML, CSS, and python, allowing users to submit requests, receive immediate feedback, and monitor error logs dynamically. The results demonstrated that the system successfully balanced security enforcement and usability, with the error-handling mechanism ensuring reliable and adaptive access control in Zero Trust IoT environments.

References:-

References

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Uchechukwu, A., Hachi, O., & Amobi Henry, M. (2025). Error Handling in Usability Model for Zero Trust Security in Internet of Things Systems. International Journal Of Mathematics And Computer Research, 13(12), 6018-6024. https://doi.org/10.47191/ijmcr/v13i12.11