With the rapid advancement in information technology, embedded systems have become integral to a wide range of critical applications. These systems often require continuous operation and cannot tolerate the downtime associated with traditional software updates, which typically necessitate a system reboot. To address this challenge, this paper proposes a novel hotpatching technique called Flexible Linked EXecution (FLEX) designed specifically for real-time embedded environments. FLEX enables seamless, dynamic firmware updates by redirecting all function calls and global variable accesses through a compile time generated Control Flow Table (CFT). In contrast to existing hotpatching approaches that depend on specialized hardware or impose strict limits on the number of patches and their size, FLEX is entirely hardware independent and supports scalable patching capacity bounded only by available flash memory and RAM. FLEX offers several unique features, including a relaxed consistency state synchronization mechanism to allow for gradual migration of program state, resolving all symbols at compile time into CFT indirections, applying updates via a double-buffered pointer swap to a new CFT, and a XIP compatible process that guarantees only a short, bounded pause time regardless of patch complexity. Experimental results show that FLEX maintains system stability and performance with an average execution overhead of about 11% and a predictable increase in flash memory usage of roughly 17%. These findings suggest that FLEX offers a robust and flexible solution for dynamically updating the software of embedded systems to ensure high availability and security without sacrificing performance.

Embedded systems rely on real-time operating systems (RTOS) for safety-critical applications, but the absence of fine-grained memory protection poses serious security risks. Existing protection mechanisms either require specialized hardware or rely on static configurations that fail to adapt to dynamic task behavior. This paper introduces Runtime Enforcement of Memory Access Protection (REMAP), which is a novel framework that combines LLVM-based static analysis with runtime Memory Protection Unit (MPU) reconfiguration to achieve fine-grained, dynamic compartmentalization for RTOS environments. REMAP constructs task-specific Access Control Table (ACT) at compile time and enforces them at runtime through on-demand MPU updates triggered by memory access violations. This lazy mapping strategy allows REMAP to support hundreds of compartments per task despite the limitation on the number of hardware MPU slots. Our prototype on ARMv8-M (Cortex-M33) demonstrates that REMAP enforces memory access control down to the function and variable level, protects shared buffers for secure Inter Process Communication (IPC), and prevents error propagation across tasks. Evaluation using the BEEBS benchmark suite shows that compartment merging significantly reduces fault frequency, yielding overheads under 5% across all benchmarks. Therefore, REMAP achieves precise, adaptive memory protection suitable for commercial RTOS deployments without compromising real-time performance.

The proliferation of Internet of Things (IoT) devices comes with many challenges among which security is one of the most serious issues. In order to address the security issue for low-end IoT devices, ARM recently proposed the platform security architecture (PSA), which provides execution isolation to safely manage and protect the computing resources of low-end IoT devices. However, developers implementing IoT services for PSA-based IoT devices need to follow complex development procedures and understand the PSA hardware, which dramatically increases the development time and cost of PSA-based IoT devices. This article analyzes vulnerabilities that may arise from general-purpose low-end IoT devices to derive the security requirements and essential security services for PSA-based IoT devices, and proposes a secure platform model based on the analysis results. The proposed secure platform model consists of System Security Services and Application Security Services based on the basic PSA model and essential trusted subsystems, and it is designed to be flexible and applicable to various types of PSA-based IoT devices. In addition, it provides secure platform services APIs to enable easy and fast development of IoT services. To evaluate the proposed secure platform model, two proof-of-concept implementations are provided by using both the basic PSA model with secure element (SE) and a reference device for ARM’s PSA. Finally, a case study shows that the development of IoT services can be done easily and quickly using the proposed security platform model.

Abstract We propose a novel approach for detecting two kinds of partial blur, defocus and motion blur, by training a deep convolutional neural network. Existing blur detection methods concentrate on designing low‐level features, but those features have difficulty in detecting blur in homogeneous regions without enough textures or edges. To handle such regions, we propose a deep encoder‐decoder network with long residual skip‐connections and multi‐scale reconstruction loss functions to exploit high‐level contextual features as well as low‐level structural features. Another difficulty in partial blur detection is that there are no available datasets with images having both defocus and motion blur together, as most existing approaches concentrate only on either defocus or motion blur. To resolve this issue, we construct a synthetic dataset that consists of complex scenes with both types of blur. Experimental results show that our approach effectively detects and classifies blur, outperforming other state‐of‐the‐art methods. Our method can be used for various applications, such as photo editing, blur magnification, and deblurring.