Physical Unclonable Functions Research Articles

Optimal depth and a novel approach to variational unitary quantum process tomography

In this work, we present two new methods for variational quantum circuit (VQC) process tomography (PT) onto n qubits systems: unitary PT based on VQCs (PT_VQC) and unitary evolution-based variational quantum singular value decomposition (U-VQSVD). Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for unitary PT and decreases the required state initializations from 4 n to just 2 n , all while ensuring high-fidelity reconstruction of the targeted unitary channel U. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration step compared to quantum deep neural network and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a universal channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel quantum physical unclonable function. By using U-VQSVD we outperform an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.

Heavy ion-induced microdose effects on the reliability of planar and FinFET-based SRAM physical unclonable functions

Heavy ion-induced microdose effects on the reliability of planar and FinFET-based SRAM physical unclonable functions

Physical unclonable function using photonic spin Hall effect

This study presents a novel method leveraging surface wave-assisted photonic spin Hall effect (PSHE) to construct physical unclonable functions (PUFs). PUFs exploit inherent physical variations to generate unique Challenge–Response pairs, which are critical for hardware security and arise from manufacturing discrepancies, device characteristics, or timing deviations. We explore PSHE generation-based PUF design, expanding existing design possibilities. With recent applications in precise sensing and computing, PSHE offers promising performance metrics for our proposed PUFs, including an inter-Hamming distance of 47.50% , an average proportion of unique responses of 62.5% , and a Pearson correlation coefficient of − 0.198. The PUF token demonstrates robustness to simulated noise. Additionally, we evaluate security using a machine learning-based attack model, employing a multi-layer perceptron (MLP) regression model with a randomized search method. The average accuracy of successful attack prediction is 9.70% for the selected dataset. Our novel PUF token exhibits high non-linearity due to the PSHE effect, resilience to MLP-based attacks, and sensitivity to process variation.

PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks

A secure and effective authentication and communication scheme between users and underwater sensors plays an important role in improving the detection and utilization of marine resources in underwater acoustic networks (UANs). However, due to the energy limitations and susceptibility to capture of underwater sensors and gateways, it is necessary to design a lightweight authentication protocol that can resist capture of sensors and gateways during attacks. In this paper, a lightweight authentication protocol for UANs based on the Physical Unclonable Function (PUF) and chaotic map is proposed. We used the advantages of PUF to resist sensors and gateways being captured in attacks and the chaotic map to achieve lightweight authentication because the computational cost of the chaotic map is almost one-third that of Elliptic Curve Cryptography (ECC). Additionally, we used the formal security proof in the random oracle model to prove the security of the proposed scheme. Our scheme was more secure and efficient compared with some other related schemes in terms of security and performance requirements, and the proposed scheme is suitable for UANs.

Physical Unclonable Functions with Hyperspectral Imaging System for Ultrafast Storage and Authentication Enabled by Random Structural Color Domains.

Physical unclonable function (PUF) is attractive in modern encryption technologies. Addressing the disadvantage of slow data storage/authentication in optical PUF is paramount for practical applications but remains an on-going challenge. Here, a highly efficient PUF strategy based on random structural color domains (SCDs) of cellulose nanocrystal (CNC) is proposed for the first time, combing with hyperspectral imaging system (HIS) for ultrafast storage and authentication. By controlling the growth and fusion behavior of the tactoids of CNC, the SCDs display an irregular and random distribution of colors, shapes, sizes, and reflectance spectra, which grant unique and inherent fingerprint-like characteristics that are non-duplicated. Based on images and spectra, these fingerprint features are used to develop two sets of PUF key generation methods, which can be respectively authenticated at the user-end and the manufacturer-front-end that achieving a high coding capacity of at least 22304. Notably, the use of HIS greatly shortens the time of key reading and generation (≈5s for recording, 0.5-0.7s for authentication). This new optical PUF labels can not only solve slow data storage and complicated authentication in optical PUF, but also impulse the development of CNC in industrial applications by reducing color uniformity requirement.

Electrospray deposition of physical unclonable functions for drug anti-counterfeiting

In recent years, pharmaceutical counterfeiting has become an increasingly dangerous situation. A patient who unknowingly consumes a counterfeit drug is at a serious health risk. To address this problem, a low-cost and robust approach for authentication that can be administered at the point-of-care is required. Our proposed solution uses Optical Physical Unclonable Functions (PUFs); patterns formed by a stochastic process that can be used for authentication. We create edible PUFs (ePUFs) using electrospray deposition, which utilizes strong electric fields to atomize a liquid suspension into a plume of micro-scale droplets that are delivered to the target. The ePUFs are electrospray-deposited from an edible ink directly onto the surface of the drug tablets. The process parameters (flow rate, translation speed, and suspension concentration) govern the characteristics of the ePUF to provide highly stochastic patterns. To evaluate our approach, 200 ePUFs were deposited onto tablets at various conditions, followed by imaging and storage of the patterns in a database. For ePUF authentication, a machine vision approach was created using the open source SIFT pattern matching algorithm. Using optimized pattern-matching constraints, our algorithm was shown to be 100% successful in authenticating the cellphone images of the ePUFs to the database. Additionally, the algorithm was found to be robust against changes in illumination and orientation of the cellphone images.

Near-Infrared Circularly Polarized Luminescent Physical Unclonable Functions.

Distinguished from traditional physical unclonable functions (PUFs), optical PUFs derive their encoded information from the optical properties of materials, offering distinct advantages, including solution processability, material versatility, and tunable luminescence performance. However, existing research on optical PUFs has predominantly centered on visible photoluminescence, while advanced optical PUFs based on higher-level covert light remain unexplored. In this study, we present optical PUFs based on the utilization of the covert light of near-infrared circularly polarized luminescence (NIR-CPL). This interesting property is achieved by incorporating Yb-doped metal halide perovskite nanocrystals (Yb-PeNCs) possessing NIR emission property into chiral imprinted photonic (CIP) films. By employing a solvent immersion method, we successfully integrated Yb-PeNCs into these CIP films, thereby creating an optically unclonable surface. The resulting NIR-CPL emission adds a layer of advanced security to the optical PUF systems. These findings underscore the potential of solution-processable chiral films to play a pivotal role in advancing the next generation of PUFs.

Robustly reusable fuzzy extractor from isogeny

Robustly reusable Fuzzy Extractor (rrFE) allows multiple extractions from the same fuzzy source in a reproducible way. The reusability of rrFE asks the pseudo-randomness of the extracted keys, while robustness of rrFE makes sure that active attacks can be detected during the reproduction of the extracted key. With rrFE, we are able to produce cryptographic keys for our cryptosystems from fuzzy sources, like biometrics, physical unclonable functions, etc. There are rrFE schemes from the DDH, LWE and LPN assumptions. However there is no rrFE scheme from isogeny-based assumptions up to now.In this paper, we construct the first rrFE from isogeny. To obtain such an rrFE, we propose a new framework for constructing rrFE with a core technical tool, named Enhanced Effective Group Action (EEGA). EEGA is built upon the basic Effective Group Action (EGA), and is equipped with a sampling algorithm and a derivation algorithm. With such an EEGA, the same random input can be derived to produce different pseudo-random and unpredictable outputs. The EEGA, together with other routine building blocks like secure sketch and extractor, leverages an FE to achieve reusability and robustness. We construct EEGA based on CSI-FiSh, which admits the first rrFE scheme from isogeny. Besides, we also propose another two EEGA instantiations from the DDH assumption, and this provides another approach to DDH-based rrFE, which may be of independent interest.

Robust PUF Label Authentication System Synergistically Constructed by Hierarchical Pattern of Self‐assembled Phase‐Separation Encrypted Wrinkle and Deep Learning Model

AbstractWith the increasing demand for information security, current anti‐counterfeiting methods not only suffer from issues such as low information density and vulnerability to forgery, but also inherently involve a trade‐off between information capacity and readout methods. This paper reports a high‐security solution using hierarchical pattern with perfect combination of micro self‐wrinkle and nano phase‐separation as physical unclonable function (PUF) labels, which is generated through self‐organization of anthracene‐functionalized poly(styrene‐block‐butadiene‐block‐styrene) (SBS‐CAN) under UV exposure. The double‐layer morphologies formed by the wrinkle and phase separation are adjustable, independent, and stable. The obtained hierarchical PUF labels exhibit random and unique features similar to the minutiae of fingerprints at both micro and nano scales, ensuring a well‐balanced bit uniformity (>0.492), high uniqueness (>0.496), and outstanding reliability (>96%). As a consequence of the multi‐layered combination of morphologies, the designed PUF label possesses an information density about 1010 times higher than that of human fingerprints. The PUF labels can be quickly obtained through simple visual scanning and exhibit sufficient security. To cope with various application environments, the advanced authentication pipeline designed LPLA guarantees robust label recognition capability in real‐world scenarios. A practical integrated anti‐counterfeiting authentication system is developed by combining hierarchical PUF labels and authentication pipeline.

Dynamic Physical Unclonable Function Relying on Lasing Polarization

Dynamic Physical Unclonable Function Relying on Lasing Polarization

A Hardware Security Protection Method for Conditional Branches of Embedded Systems.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) attacks. Moreover, to prevent attackers from stealing the critical information of branches, the method designs the hybrid arbiter physical unclonable function (APUF) circuit to encrypt and decrypt the directions, addresses, and indexes of branches. This circuit combines the advantages of double APUF and Feed-Forward APUF, which can enhance the randomness of output response and resist machine learning attacks. If attackers still successfully tamper with the branches and disrupt the control flow integrity (CFI), this method detects tampering with the instruction codes, jump addresses, and jump directions in a timely manner through dynamic and static label comparison. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment.

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.

44‐1: Top‐Emitting Quantum‐Dot Light‐Emitting Diodes with Rainbow Emission Color and Their Application in Anti‐Counterfeiting Recognition

We have successfully developed an advanced and highly secure anti‐counterfeiting device based on quantum‐dot light‐emitting diodes (QLEDs). This novel device exhibits several key attributes, including affordability, non‐replicability, ease of detection, and exceptional security. By incorporating both electroluminescence and photoluminescence modes, our device offers versatility in satisfying different levels of anti‐counterfeiting needs. To enhance its security further, we have integrated physically unclonable functions (PUFs) and combined them with a pattern recognition system. This integration ensures the device's uniqueness and enables it to meet the most stringent anti‐counterfeiting requirements. The achieved results demonstrate the significant potential of our device in combating counterfeiting and safeguarding various industries.

Addressing Vulnerabilities in CAN-FD: An Exploration and Security Enhancement Approach

The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems.

A Plasmonic Optoelectronic Resistive Random-Access Memory for In-Sensor Color Image Cryptography.

The optoelectronic resistive random-access memory (RRAM) with the integrated function of perception, storage and intrinsic randomness displays promising applications in the hardware level in-sensor image cryptography. In this work, 2D hexagonal boron nitride based optoelectronic RRAM is fabricated with semitransparent noble metal (Ag or Au) as top electrodes, which can simultaneous capture color image and generate physically unclonable function (PUF) key for in-sensor color image cryptography. Surface plasmons of noble metals enable the strong light absorption to realize an efficient modulation of filament growth at nanoscale. Resistive switching curves show that the optical stimuli can impede the filament aggregation and promote the filament annihilation, which originates from photothermal effects and photogenerated hot electrons in localized surface plasmon resonance of noble metals. By selecting noble metals, the optoelectronic RRAM array can respond to distinct wavelengthsand mimic the biological dichromatic cone cells to perform the color perception. Due to the intrinsic and high-quality randomness, the optoelectronic RRAM can produce a PUF key in every exposure cycle, which can be applied in the reconfigurable cryptography. The findings demonstrate an effective strategy to build optoelectronic RRAM for in-sensor color image cryptography applications.

PUF-Based Digital Money with Propagation-of-Provenance and Offline Transfers Between Two Parties

Building on prior concepts of electronic money (eCash), we introduce a digital currency where a physical unclonable function (PUF) engenders devices with the twin properties of being verifiably enrolled as a member of a legitimate set of eCash devices and of possessing a hardware-based root-of-trust. A hardware-obfuscated secure enclave (HOSE) is proposed as a means of enabling a PUF-based propagation-of-provenance (POP) mechanism, which allows eCash tokens ( eCt ) to be securely signed and validated by recipients without incurring any third party dependencies at transfer time. The POP scheme establishes a chain of custody starting with token creation, extending through multiple bilateral in-field transactions, and culminating in redemption at the token-issuing authority. A lightweight mutual-zero-trust (MZT) authentication protocol establishes a secure channel between any two fielded devices. The POP and MZT protocols, in combination with the HOSE, enables transitivity and anonymity of eCt transfers between online and offline devices.

Volatile threshold switching devices for hardware security primitives: Exploiting intrinsic variability as an entropy source

In the age of the Internet of Things, the proliferation of edge devices has resulted in a significant increase in personal information that is susceptible to theft and counterfeiting at various stages of data communication. As a result, substantial attention has been focused on hardware (HW) security elements, such as the true random number generator and physical unclonable function. With the recent surge in research and development of emerging memristors, which exploit the inherent variability of these devices, there has been a notable increase in studies on HW security. Particularly, volatile threshold switch (TS) devices, which exhibit insulator/metal characteristics below/above a certain threshold voltage, show great promise as security devices due to their lower power consumption and higher cycling endurance compared to nonvolatile memory devices. Despite the promising attributes and increasing demand for TS devices for HW security, there remains a lack of a comprehensive overview covering various TS devices and their potential contributions to HW privacy. To address this gap, this review provides an encompassing analysis of different types of TS devices and their performance in HW security literature, providing insight into current limitations and the future prospects of HW security primitives based on TS devices.

MAG-PUFs: Authenticating IoT devices via electromagnetic physical unclonable functions and deep learning

The challenge of authenticating Internet of Things (IoT) devices, particularly in low-cost deployments with constrained nodes that struggle with dynamic re-keying solutions, renders these devices susceptible to various attacks. This paper introduces a robust alternative mitigation strategy based on Physical-Layer Authentication (PLA), which leverages the intrinsic physical layer characteristics of IoT devices. These unique imperfections, stemming from the manufacturing process of IoT electronic integrated circuits (ICs), are difficult to replicate or falsify and vary with each function executed by the IoT device. We propose a novel lightweight authentication scheme, MAG-PUFs, that uses the unintentional Electromagnetic (EM) emissions from IoT devices as Physical Unclonable Functions (PUFs). MAG-PUFs operate by collecting these unintentional EM emissions during the execution of pre-defined reference functions by the IoT devices. The authentication is achieved by matching these emissions with profiles recorded at the time of enrollment, using state-of-the-art Deep Learning (DL) approaches such as Neural Networks (NN) and Autoencoders. Notably, MAG-PUFs offer compelling advantages: (i) it preserves privacy, as it does not require direct access to the IoT devices; and, (ii) it provides unique flexibility, permitting the selection of numerous and varied reference functions. We rigorously evaluated MAG-PUFs using 25 Arduino devices and a diverse set of 325 reference function classes. Employing a DL framework, we achieved a minimum authentication F1-Score of 0.99. Furthermore, the scheme’s efficacy in detecting impostor EM emissions was also affirmed, achieving a minimum F1-Score of 0.99. We also compared our solution to other solutions in the literature, showing its remarkable performance. Finally, we discussed code obfuscation techniques and the impact of Radio Frequency (RF) interference on the IoT authentication process.

Surveying Emerging Trends in DDoS Defense

This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise. Keywords— Cloud computing, Data threats, Data Protection, Cloud security.

Robust Optical Physical Unclonable Function Based on Total Internal Reflection for Portable Authentication.

Physical unclonable functions (PUFs) utilize uncontrollable manufacturing randomness to yield cryptographic primitives. Currently, the fabrication of the most generally employed optical PUFs mainly depends on fluorescent, Raman, or plasmonic materials, which suffer inherent robustness issues. Herein, we construct an optical PUF with high environmental stability via total internal reflection (TIR-PUF) perturbed by randomly distributed polymer microspheres. The response image is transformed into encoded keys via an iterative binning procedure. The concentration of the polymer solution is optimized to debias the bit nonuniformity and maximize encoding capacity. The constructed TIR-PUF shows significantly high encoding capacity (2370) and markedly low total authentication error probability (1.614 × 10-23). The intra-Hamming distance is as low as 0.068, indicating the excellent readout reliability of TIR-PUF. The environmental stability of TIR-PUF has demonstrated promising results under a range of challenging conditions such as ultrasonic washing, high temperature, ultraviolet irradiation, and severe chemical environments. Moreover, the challenge-response pairs of our TIR-PUFs are demonstrated on an authentication system with low-power dissipation, lightweight components, and wireless imaging capture, rendering the possibility of portable authentication for practical applications.