A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

Area of Science:

• Cybersecurity in Healthcare
• Internet of Health Care Things (IoHT)
• Data Architecture and Management

Background:

• The healthcare industry is increasingly digital, with Internet of Health Care Things (IoHT) devices offering scalable, cost-effective remote services.
• However, rapid IoHT deployment often overlooks security, leading to vulnerabilities exploited by cybercriminals, particularly during the COVID-19 pandemic.
• Weak password management and lack of multifactor authentication in IoHT systems result in significant data breaches and financial losses.

Approach:

• This viewpoint proposes a theoretical framework for an IoHT data model taxonomy to enhance adaptive multifactor authentication (AMFA).
• A comprehensive review of IoHT literature from 2012-2022 was conducted to identify cybersecurity challenges and data management strategies.
• The focus is on developing a data model that addresses the specific security requirements of IoHT users, environments, and devices.

Key Points:

• Identified significant cybersecurity threats in healthcare authentication, primarily data breaches stemming from weak security and poor IoHT device configuration.
• Highlighted the need for robust authentication mechanisms, advocating for a shift towards adaptive multifactor authentication (AMFA).
• Emphasized the critical role of a data model taxonomy for improving the feasibility and effectiveness of AMFA in IoHT environments.

Conclusions:

• A structured data taxonomy is essential for understanding and improving user authentication in modern IoHT systems.
• The proposed data model framework supports the automation of AMFA, enhancing security for healthcare users and devices.
• Addressing IoHT data architecture and security requirements is crucial for mitigating cyber threats and ensuring data privacy in remote healthcare settings.