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By A.J. Roan
Metal Tech News 

Graphene makes cybersecurity more secure

Penn State develops reliable, resilient digital fingerprint device Metal Tech News – May 12, 2021

 

Last updated 5/25/2021 at 3:35pm

graphene cybersecurity Pennsylvania State University PUF cryptographic keys

Jennifer McCann/Penn State

Penn State researchers have developed a new hardware security device that takes advantage of the microstructure variations of graphene to generate secure keys.

In the world of cybersecurity, innovations that offer robust protection of private data from ill-intentioned people are becoming more crucial. Now, researchers at Pennsylvania State University have leveraged graphene's unique properties to design a way to make encryption harder to crack.

Current silicon-based technologies exploit microscopic differences between computing components to create secure keys. However, artificial intelligence can predict these keys, thereby gaining access to important personal data.

Led by assistant professor of engineering science and mechanics Saptarshi Das, Pennsylvania State researchers have developed a novel low-power, scalable, and reconfigurable hardware security device with significant resilience to AI attacks using graphene.

"There has been more and more breaching of private data recently," said Das. "We developed a new hardware security device that could eventually be implemented to protect these data across industries and sectors."

This device is known as a physically unclonable function or PUF and is typically used as a physically defined "digital fingerprint." PUFs are most often based on unique variations of the mechanical structure that occur naturally during semiconductor manufacturing and are usually implemented in integrated circuits. Typically, these devices are used in applications with high-security requirements, such as cryptography.

Since 2010, PUFs gained attention in the smartcard market as a promising way to provide "silicon fingerprints," creating unique cryptographic keys that are one-of-a-kind to individual smartcards.

However, it was not long before methods to bypass or break such defenses became available – current technologies have not been able to stay ahead of techniques to break them, leading to more frequent security breaches.

"Normally, once a system's security has been compromised, it is permanently compromised," said Akhil Dodda, an engineering sciences and mechanics graduate under Das. "We developed a scheme where such a compromised system could be reconfigured and used again, adding tamper resistance as another security feature."

It was the physical and electrical properties of graphene and the fabrication process that allowed the novel graphene PUF to become more energy-efficient, scalable, and secure against AI attacks that would ordinarily post a threat to conventional silicon PUFs.

To develop this graphene PUF, the team first fabricated 2,000 identical graphene transistors – the physical component that acts as an on-off switch for electrical currents that allow electronics to communicate – to test the PUFs resilience to machine learning.

Despite the structural similarity between all 2,000 graphene transistors, the conductivity varied due to inherent randomness that arose from the production process. Usually, this would be a drawback for electronic devices; however, this was a desirable and intended trait for security purposes.

After the graphene transistors were implemented into PUFs, the researchers at Penn modeled their characteristics to create a simulation of 64 million graphene-based PUFs. This was done to test the PUFs' security capabilities. Using machine learning, the researchers trained an AI with this simulated data to determine if the system could make predictions about the encrypted data, possibly cracking the code.

"Neural networks are very good at developing a model from a huge amount of data, even if humans are unable to," said Das. "We found that AI could not develop a model, and it was not possible for the encryption process to be learned."

physically unclonable function cryptography AI machine learning resilience

Centre for Secure Information Technologies

This resistance to AI is what makes this PUF more secure, as "potential hackers could not use breached data to reverse engineer a device for future exploitation," added Das. More so, even if a key could be predicted, the graphene PUF can generate a new key through a reconfiguration process that requires no additional hardware or replacement of components.

With this capability and the capacity to operate across a wide range of temperatures, the novel graphene-based PUF can be used in various applications. More research is being conducted to determine the full potential of such a technology, possibly opening pathways for its use in flexible and printable electronics, household devices, and more.

 

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