"This thesis deals with the memory safety issue in life-critical medical devices. Over the last few years, several vulnerabilities such as memory exploits have been identified in various Internet of Medical Things (IoMT) devices. In the worst case, such vulnerabilities allow an attacker to remotely force an application to execute malicious actions. While many countermeasures against software exploits have been proposed so far, only a few of them seem to be suitable for medical devices. Indeed, these devices are constrained by their size, real-time performances, and safety requirements making the integration of security challenging. To address this issue, the thesis proposes two approaches. Both address the memory safety issue from the software design-time to its run-time on the hardware. A first approach assumes that memory defenses can be implemented both in hardware and software. This approach results in TrustFlow, a framework composed of a compiler able to generate secure code for an extended processor that can prevent, detect, log, and self-heal critical applications from memory attacks. The second approach considers that hardware is immutable. Following this constraint, defenses only rely upon software. This second approach results in BackGuard a modified compiler that efficiently
hardens embedded applications while ensuring control-flow integrity."