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Physical Unclonable Functions (PUFs) leverage manufacturing process imperfections that cause propagation delay discrepancies for the signals traveling along these paths. While PUFs can be used for device authentication and chip-specific key generation, strong PUFs have been shown to be vulnerable to machine learning modeling attacks. Although there is an impression that combinational circuits must be designed without any loops, cyclic combinational circuits have been shown to increase design security against hardware intellectual property theft. In this paper, we introduce feedback signals into traditional delay-based PUF designs such as arbiter PUF, ring oscillator PUF, and butterfly PUF to give them a wider range of possible output behaviors and thus an edge against modeling attacks. Based on our analysis, cyclic PUFs produce responses that can be binary, steady-state, oscillating, or pseudo-random under fixed challenges. The proposed cyclic PUFs are implemented in field programmable gate arrays, and their power and area overhead, in addition to functional metrics, are reported compared with their traditional counterparts. The security gain of the proposed cyclic PUFs is also shown against state-of-the-art attacks.
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Electromagnetically unclonable functions generated by non-Hermitian absorber-emitter
Physically unclonable functions (PUFs) are a class of hardware-specific security primitives based on secret keys extracted from integrated circuits, which can protect important information against cyberattacks and reverse engineering. Here, we put forward an emerging type of PUF in the electromagnetic domain by virtue of the self-dual absorber-emitter singularity that uniquely exists in the non-Hermitian parity-time (PT)–symmetric structures. At this self-dual singular point, the reconfigurable emissive and absorptive properties with order-of-magnitude differences in scattered power can respond sensitively to admittance or phase perturbations caused by, for example, manufacturing imperfectness. Consequently, the entropy sourced from inevitable manufacturing variations can be amplified, yielding excellent PUF security metrics in terms of randomness and uniqueness. We show that this electromagnetic PUF can be robust against machine learning–assisted attacks based on the Fourier regression and generative adversarial network. Moreover, the proposed PUF concept is wavelength-scalable in radio frequency, terahertz, infrared, and optical systems, paving a promising avenue toward applications of cryptography and encryption.
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- Award ID(s):
- 2229659
- PAR ID:
- 10504404
- Publisher / Repository:
- AAAS
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 9
- Issue:
- 36
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Amsaad, F; Abdelgawad, A; Jamil, A (Ed.)Fault Injection attack is a type of side-channel attack on the Physical Unclonable Function (PUF) module that can induce faults in the PUF response by manipulating the PUF circuit behavior through voltage glitches, laser attacks, temperature manipulations, or any other attacks potentially leading to information loss or security system failure. This type of attack exposes the physical characteristics of PUFs that can be analyzed to predict or compromise the unique challenge response pairs (CRPs) reducing the security and reliability of the PUF. Mitigation strategies against such attacks typically include adding noise to the PUF output, using error-correcting codes, or enhanced cryptographic protocols that obscure physical side-channel attacks. In this research, we propose a Generative Adversarial Network (GAN) based security model, that monitors the PUF behavior and detects the variations in PUF response. The model can detect glitches in the PUF response and generate alerts to take mitigation measures.more » « less
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Abstract With the fast growth of the number of electronic devices on the internet of things (IoT), hardware‐based security primitives such as physically unclonable functions (PUFs) have emerged to overcome the shortcomings of conventional software‐based cryptographic technology. Existing PUFs exploit manufacturing process variations in a semiconductor foundry technology. This results in a static challenge–response behavior, which can present a long‐term security risk. This study shows a reconfigurable PUF based on nanoscale magnetic tunnel junction (MTJ) arrays that uses stochastic dynamics induced by voltage‐controlled magnetic anisotropy (VCMA) for true random bit generation. A total of 100 PUF instances are implemented using 10 ns voltage pulses on a single chip with a 10 × 10 MTJ array. The unipolar nature of the VCMA mechanism is exploited to stabilize the MTJ state and eliminate bit errors during readout. All PUF instances show entropy close to one, inter‐Hamming distance close to 50%, and no bit errors in 104repeated readout measurements.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.adg7481
