skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A Permutation Challenge Input Interface for Arbiter PUF Variants Against Machine Learning Attacks
Internet of Things (IoT) have broad and deep penetration into our society, and many of them are resource-constrained, calling for lightweight security protocols. Physical unclonable functions (PUFs) leverage physical variations of circuits to produce responses unique for individual devices, and hence are not reproducible even by their manufacturers. Implementable with simplistic circuits and operable with low energy, PUFs are promising candidates as security primitives for resource-constrained IoT devices. Arbiter PUF (APUF) and its variants are lightweight in resource requirements but suffer from vulnerability to machine learning attacks. To defend APUF variants against machine learning attacks, in this paper we investigate a challenge input interface, which incurs low overhead. Analytical and experimental studies were carried out, showing substantial improvement of resistance against machine learning attacks when a PUF is equipped with the interface, rendering interfaced APUF variants promising candidates for security critical applications.  more » « less
Award ID(s):
2103563
PAR ID:
10415508
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Page Range / eLocation ID:
418 to 421
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, 2021 IEEE International Conference on Networking, Architecture and Storage (NAS))
    Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space. 
    more » « less
  2. ; (, IEEE)
    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. 
    more » « less
  3. ; ; ; ; (, 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI))
    Internet of Things (IoT) devices are mostly small and operate wirelessly on limited battery supply, and therefore have stringent constraints on power consumption and hardware resources. Therefore, energy-efficient (low energy) design is paramount for the successful deployment of resource constrained IoT devices. Further, Physical Unclonable Functions (PUFs) have evolved as a popular hardware security primitive for low cost, mass produced IoT devices with very constrained resources. Energy harvesting technologies utilizing solar cells are being used in ultra-low power IoT devices to satisfy the energy requirement. In this paper, we utilize the intrinsic variations in solar cells to design a novel solar cell based PUF. As a proof of concept, we have used the Tiva TM4C123GH6PM microcontroller to build our solar cell based PUF. From our experiments, we found that the proposed solar cell based PUF has the uniformity value of 49.21% which is close to the ideal value of 50%. Further, the proposed solar cell based PUF has worst case reliabilities of 92.97% and 90.62% with variations in temperature and light intensity, respectively. 
    more » « less
  4. ; ; ; ; ; ; ; ; ; ; et al (, APL Photonics)
    The hallmark of the information age is the ease with which information is stored, accessed, and shared throughout the globe. This is enabled, in large part, by the simplicity of duplicating digital information without error. Unfortunately, an ever-growing consequence is the global threat to security and privacy enabled by our digital reliance. Specifically, modern secure communications and authentication suffer from formidable threats arising from the potential for copying of secret keys stored in digital media. With relatively little transfer of information, an attacker can impersonate a legitimate user, publish malicious software that is automatically accepted as safe by millions of computers, or eavesdrop on countless digital exchanges. To address this vulnerability, a new class of cryptographic devices known as physical unclonable functions (PUFs) are being developed. PUFs are modern realizations of an ancient concept, the physical key, and offer an attractive alternative for digital key storage. A user derives a digital key from the PUF’s physical behavior, which is sensitive to physical idiosyncrasies that are beyond fabrication tolerances. Thus, unlike conventional physical keys, a PUF cannot be duplicated and only the holder can extract the digital key. However, emerging machine learning (ML) methods are remarkably adept at learning behavior via training, and if such algorithms can learn to emulate a PUF, then the security is compromised. Unfortunately, such attacks are highly successful against conventional electronic PUFs. Here, we investigate ML attacks against a nonlinear silicon photonic PUF, a novel design that leverages nonlinear optical interactions in chaotic silicon microcavities. First, we investigate these devices’ resistance to cloning during fabrication and demonstrate their use as a source of large volumes of cryptographic key material. Next, we demonstrate that silicon photonic PUFs exhibit resistance to state-of-the-art ML attacks due to their nonlinearity and finally validate this resistance in an encryption scenario. 
    more » « less
  5. ; ; ; ; ; (, Science Advances)
    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. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email