Radiation-induced transient faults affecting integrated circuits pose significant reliability challenges for safety-critical information systems. While hardware-based mitigation is traditional, it often precludes the use of cost-effective Commercial-Off-The-Shelf (COTS) components due to competing non-functional requirements. Software-Implemented Hardware Fault Tolerance (SIHFT) offers a flexible and cost-effective alternative. This work evaluates compiler-based SIHFT techniques that provide automated, transparent protection, achieving hardware-level resilience through software-only transformations with minimal user intervention.

System-on-chip security architecture is a critical, complex, and time-consuming activity, consuming months of effort. Furthermore, the architectural design can include subtle errors that compromise the security of the entire system. In this article, we develop a security engine infrastructure, SEnTry , for systematically creating security architectures for protecting SoC designs against a variety of security subversions. SEnTry provides a plug-and-play, configurable subsystem composed of custom IPs that can be integrated into the platform to derive different security primitives. We develop an instance of SEnTry for supply-chain attacks. We discuss the spectrum of challenges involved in developing a unified architecture for systematic protection against the variety of attacks involved and the SEnTry approach to addressing them. We provide several case studies to demonstrate SEnTry design and perform extensive experiments to evaluate its overhead on multiple ASIC technologies. Our experiments suggest that SEnTry incurs minimal overhead in area and power consumption.

As global hardware manufacturing expands and the need to meet time-to-market demands grows, companies increasingly depend on the zero-trust model to manage most of the design process for Integrated Circuits (ICs). Consequently, valuable hardware intellectual property (IP) becomes vulnerable to piracy, cloning, reverse engineering (RE), and unauthorized modifications throughout the global IC supply chain. Current security solutions are designed to safeguard the designs and protect sensitive information throughout the design flow. In this study, we present a thorough evaluation of various IP countermeasure techniques, including Logic Locking, State Space Transformation, Fine-grain Hardware Redaction, and NoC Routing Table Configuration, described in the existing literature. Inspired by cybersecurity competitions and community evaluation efforts, we designed a collaborative benchmarking initiative to evaluate these techniques on open-source benchmark designs, unveil the gaps and limitations of each technique, and discover crucial paths for future exploration. Through a retrospective analysis of this initiative, the insights we gained can significantly contribute to improving countermeasure techniques in the broad domain of hardware security and trust.

The spread of mosquito-borne diseases is accelerating owing to several factors, including human-made ones. In response, scientists are developing various types of baits, including organic, chemical, and electronic, to lure mosquitoes and monitor, control, and kill them. These baits function by replicating stimuli that attract mosquitoes, including human breath, body heat, color, and mosquito wingbeat sounds. Wingbeat sound baits generated from the wings of female mosquitoes are particularly effective in attracting males. Carbon dioxide is a potent attractant and the closest human mimic among the available mimics. Human metabolism, pregnancy, skin temperature, microbiomes, and other factors all play significant roles in the attractiveness of humans to mosquitoes. Effective bait design requires a comprehensive understanding of mosquito behavior, including feeding, mating, and oviposition patterns. Lab, semifield, and field experimental studies allow researchers to isolate behavioral responses to specific stimuli before evaluating their effectiveness under more realistic ecological conditions. Optimizing the baiting system power maximizes efficiency and success in baiting mosquitoes. The future of mosquito bait lies in automating and digital twinning of baiting systems and in prioritizing human and environmental safety.

As automotive systems increasingly incorporate electronics, software, and sensors, they become complex and widely distributed cyber-physical systems. This complexity makes them more susceptible to cyber-attacks. Nevertheless, despite its great need, the cybersecurity of automotive systems needs to be better understood, even by key stakeholders. This article presents an immersive virtual environment (IVE) platform that enhances the understanding of cybersecurity in automotive systems, focusing on ranging sensor attacks. By utilizing virtual reality (VR), the platform provides a hands-on experience for users to explore and comprehend cyber-attack implications. User studies were conducted to evaluate the effectiveness of the platform, revealing statistically significant improvements in participants’ knowledge, engagement, and self-efficacy related to automotive security. The findings underscore the potential of immersive learning tools in automotive security education, with IVE demonstrating a substantial impact on participants’ comprehension and interest in automotive security.

Multitenant systems have become essential for delivering scalable, flexible, and cost-effective computing services, mainly due to the widespread adoption of cloud computing. In these systems, the computing infrastructure is shared among multiple users, which helps to optimize resource usage and lower operational costs. However, this shared approach among mutually distrusting users creates a unique adversarial environment. This paper comprehensively reviews the security issues associated with these systems, highlighting how their core architectural features-resource sharing, customizability, and isolation-combined with their unique adversarial environment can introduce new vulnerabilities or amplify existing ones.We categorize common attack vectors based on adversarial roles, entry points, and their impact on the system. In addition, we review the current literature on security defenses and organize countermeasures into two main categories: detection and mitigation. Lastly, we outline emerging research directions and identify key challenges in improving the security of multitenant environments.

Modern system-on-chip (SoC) designs are increasingly vulnerable to supply chain threats such as counterfeiting, overproduction, and reverse engineering, leading to financial losses, intellectual property (IP) theft, and compromised system integrity. Existing security engines, while effective in principle, typically rely on microcontroller-based architectures that incur significant area and power overhead, making them impractical for resource-constrained devices. In this article, we present a minimally configured security engine (MCSE), a lightweight, modular, configurable security engine designed to address the most critical supply-chain threats with minimal resource consumption. We introduce the notion of minimum security, a baseline set of protection features necessary to secure SoCs under strict area and power constraints, and demonstrate how MCSE can be tailored to meet diverse system requirements. We validate our architecture through implementation on multiple ASIC technology nodes, showing favorable tradeoffs between security capability, area, and power. Our results establish MCSE as a compelling solution for integrating supply chain protection into low-power and area-sensitive SoC designs.

The convergence of digital twin (DT) technology with Internet of Things-driven smart manufacturing is driving a paradigm shift in Industry 4.0 and beyond. By enabling real-time cyber-physical integration, data-centric decision-making, and closed-loop optimization, DTs are becoming central to the evolution of manufacturing systems. This perspective article explores foundational concepts, emerging trends, and the market trajectory of DTs in manufacturing. We provide insights into the landscape of current tools and platforms supporting DT development, highlighting their capabilities and limitations. We also discuss the role, practical challenges, and future opportunities of the ISO 23247 standard in supporting interoperable and scalable DT frameworks in manufacturing. Finally, we discuss case studies with practical implementations, emphasizing the need for higher autonomy, artificial intelligence-enabled predictive analytics, robust lifecycle governance, and resilience in next-generation manufacturing ecosystems.

Digital Twin (DT) technology is widely regarded as one of the most promising tools for industry development, demonstrating substantial application across numerous cyber-physical systems. Gradually, this technology has been introduced into modern vehicular systems focusing on its application in intelligent driving, connected vehicles, automotive engineering, aircraft health, and many more. By creating dynamic, virtual replicas of physical vehicles and their associated components, DT enables unprecedented levels of analysis, simulation, and real-time monitoring, thereby enhancing performance, safety, and sustainability. This paper offers a comprehensive review, extending beyond digital twins to include various prototyping approaches for target-specific applications focusing on the smart vehicular systems across automotive, aviation, and maritime domains driving the evolution of next-generation vehicular infrastructure.

Understanding automotive security becomes more significant as the probability of the average person interacting with an autonomous vehicle accelerates— Yet, only a small group of specialized experts understands the nuances of security in automotive systems. With this paper, we address this knowledge gap by developing an exploration platform that provides hands-on experience with wheel speed sensors (WSS) and their security weaknesses. Unlike previous research that based such exploration platforms solely on hardware or exclusively on virtual reality, this platform combines the two. Users, from students to industry professionals, may experience different wheel speed sensor attacks from the attacker and victim perspectives in a virtual environment built with Unity, a game development engine. The experience uses data from a wheel speed sensor model and a solenoid attacker module coded with Arduino. We describe the development process for this wheel speed sensor exploration platform and demonstrate how it might be used to understand different attack vectors, including a new spoofing attack developed with the platform that allows the attacker to accurately control the wheel speed with a PID controller and spoofed magnetic pulses. The platform serves as a medium for researchers to create and defend against new attacks and as an educational resource to expand knowledge in the automotive security field.

Digital twin technology initially marked its presence in production and engineering, subsequently revolutionizing the healthcare sector with its groundbreaking applications. These include the creation of virtual replicas of patients and medical devices, enabling the formulation of personalized treatment plans. The rise of microcomputing, miniaturized hardware, and advanced machine-to-machine communications has laid the foundation for the Internet-of-Medical Things (IoMT), significantly transforming patient care through remote monitoring and timely diagnostics. Amid these technological strides, this paper offers a systematic review of digital twin technology’s integration within healthcare IoT, underlining its crucial role in promoting personalized medicine and tackling the pressing security challenges inherent in healthcare IoT systems. Focusing solely on the growing field of smart healthcare systems powered by IoT infrastructure, we explore the use of digital twins in digital patient modeling, the lifecycle of smart hospitals, surgical planning, medical devices, the pharmaceutical industry, and the IoMT cyber infrastructure, demonstrating their transformative potential in modern healthcare. Building on these findings, we outline key technical implications and emerging trends, highlight current challenges, and propose future research directions to advance healthcare IoT and its digital twin applications.

5G communication technology has become a vital component in a wide range of applications due to its unique advantages such as high data rate and low latency. While much of the existing research has focused on optimizing its efficiency and performance, security considerations have not received comparable attention, potentially leaving critical vulnerabilities unexplored. In this work, we investigate the vulnerability of 5G systems to bit-flipping attacks, which is an integrity attack where an adversary intercepts 5G network traffic and modifies specific fields of an encrypted message without decryption, thus mutating the message while remaining valid to the receiver. Notably, these attacks do not require the attacker to know the plaintext, and only the semantic meaning or position of certain fields would be enough to effect targeted modifications. We conduct our analysis on OpenAirInterface (OAI), an open-source 5G platform that follows the 3GPP Technical Specifications, to rigorously test the real-world feasibility and impact of bit-flipping attacks under current 5G encryption mechanisms. Finally, we propose a keystream-based shuffling defense mechanism to mitigate the effect of such attacks by raising the difficulty of manipulating specific encrypted fields, while introducing no additional communication overhead compared to the NAS Integrity Algorithm (NIA) in 5G. Our findings reveal that enhancements to 5G security are needed to better protect against attacks that alter data during transmission at the network level.

Vehicular safety is essential to protect drivers, passengers, and pedestrians from accidents caused by human error, environmental factors, or mechanical failures. Identifying mechanical failures is more critical for autonomous vehicles and fleets due to the absence of a human that typically detects abnormal behavior. This paper proposes a reliability and safety evaluation system that monitors impending failure modes on critical mechanical components such as drivetrain, suspension, and steering assemblies, providing real-time health data to users and fleet operators. Such a system is crucial for advancing vehicular safety, building public trust, and promoting the adoption of autonomous vehicles.

We present an innovative approach for designing a wearable solution that utilizes machine learning to systematically optimize the monitoring of vital signs for early detection of COVID-19 infections in symptomatic patients. This approach correlates sensor data trends with disease predictions, utilizing existing hospital patient data to enhance diagnosis accuracy. Our methodology offers a scalable, cost-effective solution to manage and prevent infectious diseases beyond COVID-19, addressing the limitations of traditional diagnostic methods. A functional prototype has been developed, supporting the effectiveness of continuous health monitoring in infection detection. The wearable continuously monitors key vitals such as body temperature, heart rate, respiratory rate, and oxygen saturation levels, providing an early warning system for timely medical intervention. This wearable device holds promise for transforming infectious disease detection and management, benefiting healthcare professionals and individuals alike.