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Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data

PUF-Based Three Factor Authentication System for Internet of Medical Things

With the advancement of technology, wireless devices, wireless communication, and devices like the Internet of Things (IoT) have managed to imprint in our daily lives. IoT is becoming a part of the internet revolution. Technology is making the possibility of bringing physical devices to the digital realm reachable. IoT systems are impacting in different aspects, from home monitoring to health monitoring of people’s daily life. The growing connectivity of medical equipment has obvious implications for healthcare, including improved chronic disease diagnosis and management for an aging global population. According to a Deloitte report, more than 500,000 medical technologies are currently available. Internet of medical things (IoMT) devices could be wearables and medical/vital monitors; strictly they are intended to be used on the bodies, in the household, community, clinic, or healthcare facilities for medical treatment; and they may include real-time location, telemedicine, and other services. With a widespread adaptation of IoMT, the data handled by the environment has become more vulnerable. Sensitive user data will be at risk if an attacker gains access to the environment with an ill intention, in certain situations, the patient life might be at risk. To protect the data and maintain integrity, the medical devices need to enroll in an authentication framework and the method needs to be robust to defend against attacks. Moreover, the framework needs to be lightweight so that resource constrain medical devices can handle it. To improve the security system and resist security vulnerabilities, a physical unclonable function (PUF)-based authentication protocol (NAHAP) is proposed. The proposed framework needs 6 ms computation time and 193 bytes for communication which is less than other existing authentication frameworks