PCB Surface Fingerprints Based Counterfeit Detection of Electronic Devices

Iqbal, Taswar; Wolf, Kai-Dietrich

doi:10.2352/issn.2470-1173.2017.7.mwsf-338

Cited by 11 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the main challenge of the adversary would be evading visual inspection. However, we expect multiple anomaly detection techniques to be employed, such as ones that detect hidden modifications [7], [31], because PDNPulse is not intended to be an ultimate solution. Trojans that harvest power from data signals are also plausible.…”

Section: B Attacker Responsementioning

confidence: 99%

See 1 more Smart Citation

PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

Zhu¹,

Shan²,

Sullivan³

et al. 2022

Preprint

View full text Add to dashboard Cite

The ubiquitous presence of printed circuit boards (PCBs) in modern electronic systems and embedded devices makes their integrity a top security concern. To take advantage of the economies of scale, today's PCB design and manufacturing are often performed by suppliers around the globe, exposing them to many security vulnerabilities along the segmented PCB supply chain. Moreover, the increasing complexity of the PCB designs also leaves ample room for numerous sneaky board-level attacks to be implemented throughout each stage of a PCB's lifetime, threatening many electronic devices. In this paper, we propose PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework that can identify a wide spectrum of boardlevel malicious modifications. PDNPulse leverages the fact that the PDN's characteristics are inevitably affected by modifications to the PCB, no matter how minuscule. By detecting changes to the PDN impedance profile and using the Frechet distancebased anomaly detection algorithms, PDNPulse can robustly and successfully discern malicious modifications across the system. Using PDNPulse, we conduct extensive experiments on seven commercial-off-the-shelf PCBs, covering different design scales, different threat models, and seven different anomaly types. The results confirm that PDNPulse creates an effective security asymmetry between attack and defense.

show abstract

Section: B Attacker Responsementioning

confidence: 99%

“…Surface imaging uses such as visible light [13], [58] and interferometry [36] to detect anomalies. Cameras or microscopes are needed to detect the change of PCB surface pattern [31] or to visually examine PCBs [14]. However, surface imaging cannot detect sophisticated counterfeits.…”

Section: Related Workmentioning

confidence: 99%

PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

Zhu¹,

Shan²,

Sullivan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Variations in the visual patterns on the surface of PCB, power consumption, temperature, and electrical impedance are a few examples of such features. A variety of physical inspection approaches (e.g., optical inspection [4,18,20,20,26,40], X-ray tomography [5], Terahertz Tomography [2], electrical inspection [16,30,47,50], and EM inspection [22]) have been developed to discover possible anomalies.…”

Section: Challenges With Existing Verification Schemesmentioning

confidence: 99%

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

Mosavirik

Ganji

Schaumont

et al. 2022

J. Emerg. Technol. Comput. Syst.

View full text Add to dashboard Cite

The globalization of electronic systems’ fabrication has made some of our most critical systems vulnerable to supply chain attacks. Implanting spy chips on the printed circuit boards (PCBs) or replacing genuine components with counterfeit/recycled ones are examples of such attacks. Unfortunately, conventional attack detection schemes for PCBs are ad-hoc, costly, unscalable, and error-prone. This work introduces a holistic physical verification framework for PCBs, called ScatterVerif , based on the characterization of the PCBs’ power distribution network (PDN). First, we demonstrate how scattering parameters, frequently used for impedance characterization of RF circuits, can characterize the entire PCB with a single measurement. Second, we present how a class of machine learning algorithms, namely the Gaussian Mixture Model, can be applied to the measurements to automatically classify/cluster the genuine and tampered/counterfeit PCBs. We show that these attacks affect the overall impedance of a PCB differently in various frequency ranges , and hence, the conventional impedance measurements using a constant-frequency electrical stimulus might leave the attack undetected. We conduct extensive experiments on counterfeit and tampered devices and demonstrate that these attacks can be detected with a high-confidence. Finally, we show that the acquired data from the PDN characterization can also be deployed for fingerprinting genuine PCBs.

show abstract

“…Other authentication techniques have been proposed that fingerprint PCB surface imperfections [19]. Moreover, visual imperfections that occur within PCB vias or through-hole solder pads are used in the fingerprinting process.…”

Section: Introductionmentioning

confidence: 99%

NotchPUF: Printed Circuit Board PUF Based on Microstrip Notch Filter

Martin

Plusquellic

2020

Cryptography

View full text Add to dashboard Cite

Physical Unclonable Functions (PUFs) are primitives that are designed to leverage naturally occurring variations to produce a random bitstring. Current PUF designs are typically implemented in silicon or utilize variations found in commercial off-the-shelf (COTS) parts. Because of this, existing designs are insufficient for the authentication of Printed Circuit Boards (PCBs). In this paper, we propose a novel PUF design that leverages board variations in a manufactured PCB to generate unique and stable IDs for each PCB. In particular, a single copper trace is used as a source of randomness for bitstring generation. The trace connects three notch filter structures in series, each of which is designed to reject specific but separate frequencies. The bitstrings generated using data measured from a set of PCBs are analyzed using statistical tests to illustrate that high levels of uniqueness and randomness are achievable.

show abstract

PCB Surface Fingerprints Based Counterfeit Detection of Electronic Devices

Cited by 11 publications

References 0 publications

PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

NotchPUF: Printed Circuit Board PUF Based on Microstrip Notch Filter

Contact Info

Product

Resources

About