Counterfeit Integrated Circuits: Detection, Avoidance, and the Challenges Ahead

Guin, Ujjwal; DiMase, Daniel; Tehranipoor, Mohammad

doi:10.1007/s10836-013-5430-8

Cited by 225 publications

(93 citation statements)

References 36 publications

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“…It is now virtually impossible to find out the origin of electronic products, and track their route in the supply chain. We have observed this far-reaching penetration of non-authentic systems and counterfeit parts into the electronics supply chain [39,40,42,84,85].…”

Section: Trust Issues In Sensor Supply Chainmentioning

confidence: 97%

TSensors Vision, Infrastructure and Security Challenges in Trillion Sensor Era

2017

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With the advancement of ubiquitous computing under the hood of Internet of Things (IoT) and Cyber-Physical Systems (CPS), the number of connected devices is expected to grow exponentially in the following decade. Pervasive sensing is the backbone of any IoT/CPS application. Billions of connected devices each having multiple sensors will lead us to the age of trillion sensors. Widespread use of sensors in critical applications (e.g., smart grid, agricultural industry, food production, etc.) will present us with unique challenges. Identifying the threats well before they occur will be the key in the race against the threats posed by the adversaries in the age of trillion sensors (TSensors). In this paper, we present a detailed survey of the trends toward trillion sensors, and their applicability in recent connected applications. We identify several key areas, that need to be addressed to build a secure connected environment. There are several challenges and limitations as well which are expected to rise in the coming decade. We must be proactive in addressing those challenges to make a safe and secure environment.

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Section: Trust Issues In Sensor Supply Chainmentioning

confidence: 97%

TSensors Vision, Infrastructure and Security Challenges in Trillion Sensor Era

2017

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“…They may affect the availability, integrity, and authenticity of the battery system in different ways. For instance, degraded ICs that are recycled, remarked, out-of-spec, or defective will not only lead to economic loss but also cause functional downgrading and even system damage [19]. Tampering can either be on the die level ("hardware Trojan"') or package level.…”

Section: Attack Modelmentioning

confidence: 99%

“…This is a huge research area and out of the scope of this survey. For this reason, we point readers to [19,51] for more information on IC security.…”

Section: Future Directionsmentioning

confidence: 99%

A Security Perspective on Battery Systems of the Internet of Things

et al. 2017

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Battery (sub)systems are used in many systems (systems-of-systems) in the Internet of Things (IoT) ranging from everyday ones (e.g., mobile systems, home appliances, etc.) to safety-critical and/or mission-critical ones (e.g., electrical vehicles, unmanned aerial vehicles, autonomous underwater vehicles, etc.). As these systems become more interconnected with each other and their environments and batteries become more energy dense, the safety risks of using batteries increase. To guarantee effectiveness and prevent potential safety threats (i.e., failure, overheating, explosion), it is not only crucial to ensure that batteries are functioning correctly (via safety circuits and battery management system), but to also prevent security threats that specifically target the battery system from different parts of these systems. A security analysis is necessary for system manufacturers and users to understand what threats and solutions exist for battery system security. In University of Florida, Gainesville, FL 32611, USA this paper, we present a security perspective on battery systems, where we use a layered approach to analyze vulnerabilities, threats, and potential effects. We divide the battery system into the Physical, Battery Management System, and Application layers and use mobile systems and cyber-physical systems as case studies for IoT applications. We then highlight and discuss some existing solutions and mention the potential research directions on battery system security.

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“…Exterior testing inspects ICs using metric based tests. The results of these tests depend largely on the subject matter expert [6], and therefore are prone to human error and lack automation. Electrical testing of integrated circuits has been presented as a means to overcome the deficits associated with physical analysis; however these methods also come with their own set of disadvantages [6].…”

Section: Introductionmentioning

confidence: 99%

“…The results of these tests depend largely on the subject matter expert [6], and therefore are prone to human error and lack automation. Electrical testing of integrated circuits has been presented as a means to overcome the deficits associated with physical analysis; however these methods also come with their own set of disadvantages [6]. Electrical parametric testing is problematic because the variance in electrical parameters between integrated circuits of the same type can be high, even if all of the samples are authentic, and changes in the electrical parameters can also be due to the age of the device rather than counterfeiting [7].…”

Section: Introductionmentioning

confidence: 99%

Real-time automated counterfeit integrated circuit detection using x-ray microscopy

et al. 2015

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Received Month X, XXXX; revised Month X, XXXX; accepted Month X, XXXX; posted Month X, XXXX (Doc. ID XXXXX); published Month X, XXXX Determining the authenticity of integrated circuits is paramount in preventing counterfeit and malicious hardware from being used in critical military, healthcare, aerospace, consumer, and industry applications. Existing techniques to distinguish between authentic and counterfeit integrated circuits often includes destructive testing requiring subject matter experts. We present a non-destructive technique to detect counterfeit integrated circuits using X-ray microscopy and advanced imaging analysis with different pattern recognition approaches. Our proposed method is completely automated, and runs in real time. In our approach, images of an integrated circuit are obtained from an X-ray microscope. Local binary pattern features are then extracted from the X-ray image followed by dimensionality reduction through principal component analysis, and alternatively through a non-linear principal component methodology using a stacked autoencoder embedded in a deep neural network. From the reduced dimension features, we train two types of learning machines, a support vector machine with a non-linear kernel, and a deep neural network. We present experiments using authentic and counterfeit integrated circuits to demonstrate that the proposed approach achieves an accuracy of 100% in distinguishing between the counterfeit and authentic samples.

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Counterfeit Integrated Circuits: Detection, Avoidance, and the Challenges Ahead

Cited by 225 publications

References 36 publications

TSensors Vision, Infrastructure and Security Challenges in Trillion Sensor Era

TSensors Vision, Infrastructure and Security Challenges in Trillion Sensor Era

A Security Perspective on Battery Systems of the Internet of Things

Real-time automated counterfeit integrated circuit detection using x-ray microscopy

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