Application of 2D Materials in Hardware Security for Internet-of-Things: Progress and Perspective

Internet-of-Things (IoT) is a ubiquitous network that features a tremendous amount of data and myriads of heterogeneous devices, which are interconnected and accessible or controllable anywhere and anytime. The security of loT is therefore unequivocally crucial in several aspects, such as device-to-device communication, sensing and actuating, and information exchange. Conventional cryptographic algorithms and silicon-based security primitives are constantly challenged by evolving methods of attack. By far, many efforts and achievements have been made using 2D materials for various electronics applications. Therefore, it is plausible to explore the implementation of hardware security using 2D materials, for example, true random number generators (TRNGs), physical unclonable functions (PUFs), camouflage, and anticounterfeit. TRNGs and PUFs are critical elements of hardware security and are widely deployed in cryptographic keys, identification, and authentication. In contrast to conventional utilization of manufacturing variations, security primitives using 2D materials have other entropy sources to exploit, such as the random nature of material growth and intrinsic randomness in charge trapping/detrapping. In this review, research progresses in 2D material-based TRNGs, PUFs, and other security applications are summarized, along with the discussion on entropy sources, reliability, circuit, and machine learning modeling attacks launched on TRNGs and PUFs.

Automated summary

The security of Internet-of-Things (IoT) is crucial in various aspects such as device-to-device communication, sensing and actuating, and information exchange. Conventional cryptographic algorithms and silicon-based security primitives are constantly challenged by evolving attack methods, thus the implementation of hardware security using 2D materials is worth exploring. This review summarizes the research progress in 2D material-based true random number generators (TRNGs), physical unclonable functions (PUFs), and other security applications, and discusses entropy sources, reliability, circuit, and machine learning modeling attacks on TRNGs and PUFs.