Mitigating Privacy and Data Security Risks in Healthcare-IoT using AI/ML techniques
In the last few years, the healthcare industry has significantly transformed with the increased use of emerging technologies (i.e., IoT, Cloud, AI/ML, AT/VR, etc.). The factors driving these needs are:
Increased demands for precision healthcare,
Remote patient monitoring,
Enhancing overall well-being, and
Reduced costs and waiting times.
This raises a major security concern as the IoT devices have not been designed with “a security-in-mind” perspective across the global IoT in the healthcare market.
The objectives of this research are:
Analysis and refinement of defined problem statement by carrying out extensive research and survey into IoT systems employed in healthcare.
Literature Review for examining the current state of cyber resilience within healthcare-IoT devices.
Identifying and assessing the main security and privacy issues in IoT-based precision healthcare.
Developing a novel DL/CNN technique for securing the H-IoT devices from novel threats and risks.
Evaluating cyber resilience (identifying, detecting, and mitigating cyber-attacks) in healthcare IoT devices to examine the effectiveness of the developed CNN technique in real-time H-IoT environment, assessing the impact and efficacy of the designed approach