Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks

Area of Science:

• Computer Science
• Cybersecurity
• Healthcare Technology

Background:

• The Internet of Things (IoT) is integral to various sectors, including healthcare, offering robust frameworks for data security, privacy, and reliability.
• Despite IoT's security protocols, intermediate attacks and unauthorized access threaten the privacy and reliability of sensitive health information.
• These vulnerabilities compromise the integrity of the entire healthcare system within the internet environment.

Purpose of the Study:

• To introduce a novel approach for mitigating malware attacks in IoT-based healthcare systems.
• To enhance the security, privacy, and reliability of medical information management.
• To address the challenges posed by intermediate attacks and intruders in the internet environment.

Main Methods:

• Implementation of Learning-based Deep-Q-Networks for malware attack reduction.
• Layered examination of medical information utilizing Q-learning principles.
• Minimization of intermediate attacks with reduced system complexity.

Main Results:

• Experimental results demonstrate the effectiveness of the proposed Deep-Q-Networks method.
• Significant reduction in malware attacks targeting health information management.
• Improved security, privacy, and reliability of the IoT healthcare system.

Conclusions:

• The Learning-based Deep-Q-Networks approach offers a promising solution for securing IoT in healthcare.
• The Q-learning concept effectively minimizes intermediate attacks, enhancing data protection.
• This research contributes to a more secure and reliable digital healthcare ecosystem.