Automated Artificial Intelligence Framework for Anomaly Detection in Healthcare SD-IoT Networks

Algamdi, Hammam; Aujla, Gagangeet Singh; Singh, Amritpal; Jindal, Anish; Trehan, Amitabh

Abstract

In healthcare IoT networks, network anomalies can disrupt the flow of reliable data, potentially compromising healthcare data's security and integrity. To address this challenge, several anomaly detection methods have been developed using artificial intelligence (AI) algorithms. However, finding an optimal AI model with the best tuning parameters for effective anomaly detection is a time-consuming and resource-intensive task. To address this issue, we propose an Automated AI (AutoAI) approach to optimize the tuning of hyperparameters in healthcare data anomaly detection. By leveraging the power of AutoAI, our goal is to streamline the anomaly detection process, making it more accurate and efficient. Our method is designed to adapt dynamically to the ever-changing nature of healthcare data, ensuring robustness against emerging anomalies. The proposed AutoAI method was validated in a realistic scenario and the outcomes depict the superiority of the proposed approach as compared to existing schemes on various performance evaluation metrics.

Citation

Algamdi, H., Aujla, G. S., Singh, A., Jindal, A., & Trehan, A. (2024, December). Automated Artificial Intelligence Framework for Anomaly Detection in Healthcare SD-IoT Networks. Presented at GLOBECOM 2024 - 2024 IEEE Global Communications Conference, Cape Town, South Africa

• Presentation Conference Type: Conference Paper (published)
• Conference Name: GLOBECOM 2024 - 2024 IEEE Global Communications Conference
• Start Date: Dec 8, 2024
• End Date: Dec 12, 2024
• Acceptance Date: Nov 1, 2024
• Online Publication Date: Mar 11, 2025
• Publication Date: Mar 11, 2025
• Deposit Date: May 24, 2025
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 3503-3508
• Book Title: GLOBECOM 2024 - 2024 IEEE Global Communications Conference
• DOI: https://doi.org/10.1109/GLOBECOM52923.2024.10901410
• Public URL: https://durham-repository.worktribe.com/output/3781142