From Statistics to Circuits: Foundations for Future Physical Unclonable Functions

Remote Quantum-Safe Authentication of Entities with Physical Unclonable Functions

Photonics ◽

10.3390/photonics8070289 ◽

2021 ◽

Vol 8 (7) ◽

pp. 289

Author(s):

Georgios M. Nikolopoulos

Keyword(s):

Authentication Protocol ◽

Authentication Protocols ◽

Photonic Networks ◽

Physical Unclonable Functions ◽

Quantum Protocols ◽

Open Question ◽

Standard Techniques

Physical unclonable functions have been shown to be a useful resource of randomness for implementing various cryptographic tasks including entity authentication. All the related entity authentication protocols that have been discussed in the literature so far, either they are vulnerable to an emulation attack, or they are limited to short distances. Hence, quantum-safe remote entity authentication over large distances remains an open question. In the first part of this work, we discuss the requirements that an entity authentication protocol has to offer, to be useful for remote entity authentication in practice. Subsequently, we propose a protocol, which can operate over large distances, and offers security against both classical and quantum adversaries. The proposed protocol relies on standard techniques, it is fully compatible with the infrastructure of existing and future photonic networks, and it can operate in parallel with other quantum protocols, including QKD protocols.

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Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks

Sensors ◽

10.3390/s21062009 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2009

Author(s):

Fatemeh Najafi ◽

Masoud Kaveh ◽

Diego Martín ◽

Mohammad Reza Mosavi

Keyword(s):

Error Correction ◽

Random Access ◽

Error Correction Codes ◽

Access Memory ◽

Deep Convolutional Neural Networks ◽

Device Identification ◽

Physical Unclonable Functions ◽

Server Side ◽

Correction Technique ◽

Reduction Methods

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

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Using Polynomial Interpolation for Reproducing Multi-Valued Responses of Physical Unclonable Functions on FPGAs

2021 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas51556.2021.9401501 ◽

2021 ◽

Author(s):

Holger Mandry ◽

Sven Muelich ◽

Joachim Becker ◽

Robert F.H. Fischer ◽

Maurits Ortmanns

Keyword(s):

Polynomial Interpolation ◽

Physical Unclonable Functions

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Resilient Password Manager Using Physical Unclonable Functions

IEEE Access ◽

10.1109/access.2021.3053307 ◽

2021 ◽

Vol 9 ◽

pp. 17060-17070

Author(s):

Mohammad Mohammadinodoushan ◽

Bertrand Cambou ◽

Christopher Robert Philabaum ◽

Nan Duan

Keyword(s):

Physical Unclonable Functions

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Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks

Electronics ◽

10.3390/electronics10050537 ◽

2021 ◽

Vol 10 (5) ◽

pp. 537

Author(s):

Hongxiang Gu ◽

Miodrag Potkonjak

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Evolution Strategies ◽

Light Weight ◽

Network Configuration ◽

System Complexity ◽

Physical Unclonable Functions ◽

Output Stability ◽

Output Mapping ◽

Weight Design

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.

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Proof-of-PUF Enabled Blockchain: Concurrent Data and Device Security for Internet-of-Energy

Sensors ◽

10.3390/s21010028 ◽

2020 ◽

Vol 21 (1) ◽

pp. 28

Author(s):

Rameez Asif ◽

Kinan Ghanem ◽

James Irvine

Keyword(s):

Hybrid Approach ◽

Future Research ◽

Data Provenance ◽

Data Generation ◽

Cyber Attack ◽

Design Development ◽

Seamless Integration ◽

Device Identification ◽

Physical Unclonable Functions ◽

History Of

A detailed review on the technological aspects of Blockchain and Physical Unclonable Functions (PUFs) is presented in this article. It stipulates an emerging concept of Blockchain that integrates hardware security primitives via PUFs to solve bandwidth, integration, scalability, latency, and energy requirements for the Internet-of-Energy (IoE) systems. This hybrid approach, hereinafter termed as PUFChain, provides device and data provenance which records data origins, history of data generation and processing, and clone-proof device identification and authentication, thus possible to track the sources and reasons of any cyber attack. In addition to this, we review the key areas of design, development, and implementation, which will give us the insight on seamless integration with legacy IoE systems, reliability, cyber resilience, and future research challenges.

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