Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives

Botero, Ulbert J.; Wilson, Ronald; Lu, Hangwei; Rahman, Mir Tanjidur; Mallaiyan, Mukhil A.; Ganji, Fatemeh; Asadizanjani, Navid; Tehranipoor, Mark M.; Woodard, Damon L.; Forte, Domenic

doi:10.48550/arxiv.2002.04210

Cited by 2 publications

(1 citation statement)

References 81 publications

(129 reference statements)

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“…Electronic Microprobe is classified as an invasive attack along with fault injection and reverse engineering, as they both require complete encapsulation removal and circuit wiring exposure. Today's most commonly used and powerful electron probe tool is the Focused Ion Beam (FIB) [60,61]. Through FIB technology, attackers can achieve sub-micron or even nano-level precision [62].…”

Section: Electron Microprobementioning

confidence: 99%

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

et al. 2023

Security and Safety

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Precision medicine provides a holistic view of a person’s health that combines genes, environment and lifestyle, aiming at realizing the individualized therapy. With the developing of Internet of Things (IOT) devices, widespread emergence of Electronic Medical Records (EMR), booming of cloud computing and artificial intelligence, it provides an opportunity to collect the healthcare big data throughout the lifespan and analyze the disease risk at all stages of life. Thus, precision medicine is shifting to the focus from treatment to prediction and prevention, namely precision health. To achieve this goal, different types of data, such as omics, imaging, EMR, continuous physiological monitoring, lifestyle, and environmental information need to be collected, tracked, managed and shared. For this purpose, Internet-of-Medical Things (IoMT) is playing a vital role in bringing together the health systems, applications, services and devices, that can improve the speed and accuracy of diagnosis and treatments, and monitor and modify patient behaviour and health status in real time. However, due to the proliferation of IoMT devices, security has become a growing concern. The increasing interconnectivity of IoMT-enabled devices with the health data reception, transmission, and processing significantly increases the number of potential vulnerabilities within a system. To address the security issues for precision health in IoMT systems, in this article, we review the state-of-the-art techniques and schemes from the perspective of a hierarchical system architecture. We present an IoMT system model consisting of three layers: the sensing layer, the network layer and the cloud infrastructure layer. In each layer, we discuss the security vulnerabilities and threats, and review the existing security techniques and schemes corresponding to the system components and their functionalities. Due to the unique nature of biometric features in medical and health services, we highlight the biometrics-based technologies applied in IoMT systems, which makes a great difference from the security solutions in other existing IoT systems. Finally, we summarize the challenges and future research directions in IoMT systems for a better and more secure future of precision health.

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Section: Electron Microprobementioning

confidence: 99%

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

et al. 2023

Security and Safety

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A Fast Object Detection-Based Framework for Via Modeling on PCB X-Ray CT Images

Koblah

Botero

Costello

et al. 2023

J. Emerg. Technol. Comput. Syst.

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For successful printed circuit board (PCB) reverse engineering (RE), the resulting device must retain the physical characteristics and functionality of the original. Although the applications of RE are within the discretion of the executing party, establishing a viable, non-destructive framework for analysis is vital for any stakeholder in the PCB industry. A widely-regarded approach in PCB RE uses non-destructive x-ray computed tomography (CT) to produce three-dimensional volumes with several slices of data corresponding to multi-layered PCBs. However, the noise sources specific to x-ray CT and variability from designers hampers the thorough acquisition of features necessary for successful RE. This article investigates a deep learning approach as a successor to the current state-of-the-art for detecting vias on PCB x-ray CT images; vias are a key building block of PCB designs. During RE, vias offer an understanding of the PCB’s electrical connections across multiple layers. Our method is an improvement on an earlier iteration which demonstrates significantly faster runtime with quality of results comparable to or better than the current state-of-the-art, unsupervised iterative Hough-based method. Compared with the Hough-based method, the current framework is 4.5 times faster for the discrete image scenario and 24.1 times faster for the volumetric image scenario. The upgrades to the prior deep learning version include faster feature-based detection for real-world usability and adaptive post-processing methods to improve the quality of detections.

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Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives

Cited by 2 publications

References 81 publications

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

A Fast Object Detection-Based Framework for Via Modeling on PCB X-Ray CT Images

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