Decoding the Solution for Man-at-the-End Attacks and Reverse Engineering on IoMT Devices: An Experimental Review of Techniques and Defences.

The Internet of Medical Things (IoMT), which revolutionizes modern healthcare, is expanding to provide remote support and treatment options. The device is handled by the patient or the healthcare provider to monitor the data continuously. When the IoMT is not handled with care by the authorized person, the sensitive data can be compromised by Man-At-The-End attacks (MATE). The gain of physical access allows the attacker to insert malicious code and tamper with the normal functionality, leading to wrong diagnosis and treatment. The altered performance of the medical device causes direct harm to patients. The existing software solutions do not provide complete security when it comes to attacks that gain physical access to devices. Physically Unclonable Functions (PUF) and Field Programmable Gate Arrays (FPGA) based security offers protection by establishing robust authentication through customizable logic, real-time data processing, and obfuscation. Identifying MATE attacks that are resistant to PUF/FPGA protection is crucial to offering a robust and resilient solution. The classification of critical MATE attacks specific to IoMT devices, Experimental validation using hardware platforms like Arduino Uno, Raspberry Pi, and ESP32 for reverse engineering is performed. Existing security measures available to protect the healthcare device, and how FPGA and PUF can provide better security against MATE attacks are explained. This paper uniquely reviews a thorough analysis of the vulnerability of PUF-enabled IoMT devices in the face of MATE attacks is presented. Possible futuristic solutions such as tamper-proof hardware, evolvable PUF, and AI-assisted anomaly detection for the identified problems, with experimental findings and security assessments, offer practical insights.