Counterfeiting in electronics is a growing fraudulent business and a challenging detecting activity. Industry 4.0 can be easily affected by counterfeit electronics, considering the massive use of physical devices and the large amounts of data generated that could be disclosed, causing safety risks and know-how loss. If not detected, counterfeit electronics can lead to software, hardware, network and information security problems and disastrous system breakdowns during field operations. The range of counterfeit electronics, from recycled to tampered parts, adds to this complexity, highlighting the need for advanced detection methods. This work aims to raise awareness about the risks of using electronics purchased from unauthorized distributors, which could lead to the procurement of counterfeit devices. It will also provide an overview of methodologies to mitigate these risks when forced to procure components from unauthorized suppliers. Finally, it will propose a non-destructive detection approach that relies on electrical measurements and machine-learning algorithms, which could be trained during in-line operation. It could offer a low-cost solution to this pervasive problem, at least for simple electronic devices.

Counterfeit electronics in industry 4.0: risks and detection

Giovanna Mura
;
Simone Carta;Giorgio Montisci;
2025-01-01

Abstract

Counterfeiting in electronics is a growing fraudulent business and a challenging detecting activity. Industry 4.0 can be easily affected by counterfeit electronics, considering the massive use of physical devices and the large amounts of data generated that could be disclosed, causing safety risks and know-how loss. If not detected, counterfeit electronics can lead to software, hardware, network and information security problems and disastrous system breakdowns during field operations. The range of counterfeit electronics, from recycled to tampered parts, adds to this complexity, highlighting the need for advanced detection methods. This work aims to raise awareness about the risks of using electronics purchased from unauthorized distributors, which could lead to the procurement of counterfeit devices. It will also provide an overview of methodologies to mitigate these risks when forced to procure components from unauthorized suppliers. Finally, it will propose a non-destructive detection approach that relies on electrical measurements and machine-learning algorithms, which could be trained during in-line operation. It could offer a low-cost solution to this pervasive problem, at least for simple electronic devices.
2025
978-981-94-3281-3
Counterfeit electronics risks; Fake electronics; Non-destructive detection; Machine learning; IoT; Industry 4.0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/453205
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