Design of Robust, High-Entropy Strong PUFs via Weightless Neural Network

Araújo, Leandro Santiago de; Patil, Vinay C.; Prado, Charles B.; Alves, Tiago A. O.; Marzulo, Leandro A. J.; França, Felipe M. G.; Kundu, Sandip

doi:10.1007/s41635-019-00071-z

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Particularly, the vulnerability of security credentials, identities, and secret keys stored in the PDC to hardware and software exploits poses a critical challenge. It is imperative for researchers to propose innovative and reliable hybrid-controlled strong PUF [60], [89], [90], [91], or Quantum-based PUF (QPUF) solutions [92] to mitigate such attacks, further strengthening the security of synchrophasor networks in the quantum era.…”

Section: Discussionmentioning

confidence: 99%

Securing Synchrophasors Using Data Provenance in the Quantum Era

Javed,

Ali Khan,

Ullah

et al. 2024

IEEE Open J. Commun. Soc.

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Trust in the fidelity of synchrophasor measurements is crucial for the correct operation of modern power grids. While most of the existing research on data provenance focuses on the Internet of Things, there is a significant need for effective malicious data detection in power systems. Current methods either fail to detect malicious data modifications or require certain Phasor Measurement Units (PMUs) to be physically secured. To solve these issues, this paper presents a new protocol to establish data provenance in synchrophasor networks. The proposed protocol is based on Physically Unclonable Functions (PUFs) and harnesses the principles of quantum unreality and uncertainty. It aims not only to verify the source of data but also to provide robust protection against data tampering. The proposed protocol serves the purpose of devising new protocols to protect our critical infrastructure sectors in the quantum era. Security and performance analyses, along with experiments conducted on IBM's Qiskit platform, demonstrate that the protocol offers a strong defense against cyberattacks while maintaining a lightweight profile. In particular, the proposed protocol has a worst-case computational complexity of O(1), an execution time per packet bounded by the time required to compute a cryptographically secure hash, and an upper bound for the per packet communication overhead of 256-bits. In terms of storage overhead, the proposed protocol requires each PMU to store the output of a cryptographically secure hash function, while the PDC needs to store one challenge-response pair (CRP) for each PMU.

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Section: Discussionmentioning

confidence: 99%

Securing Synchrophasors Using Data Provenance in the Quantum Era

Javed,

Ali Khan,

Ullah

et al. 2024

IEEE Open J. Commun. Soc.

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“…Several PUF-improvements were presented to overcome PUF-drawbacks to make them more robust, reliable and secure. Such improvements were conducted in [16], [17], [18], [19] and [20] based on error-correction techniques and/or combining PUFs with additional cryptographic primitives.…”

Section: Counteracting the Drawbacks Of Pufsmentioning

confidence: 99%

Low-Complexity Nonlinear Self-Inverse Permutation for Creating Physically Clone-Resistant Identities

2020

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New large classes of permutations over ℤ 2 n based on T-Functions as Self-Inverting Permutation Functions (SIPFs) are presented. The presented classes exhibit negligible or low complexity when implemented in emerging FPGA technologies. The target use of such functions is in creating the so called Secret Unknown Ciphers (SUC) to serve as resilient Clone-Resistant structures in smart non-volatile Field Programmable Gate Arrays (FPGA) devices. SUCs concepts were proposed a decade ago as digital consistent alternatives to the conventional analog inconsistent Physical Unclonable Functions PUFs. The proposed permutation classes are designed and optimized particularly to use non-consumed Mathblock cores in programmable System-on-Chip (SoC) FPGA devices. Hardware and software complexities for realizing such structures are optimized and evaluated for a sample expected target FPGA technology. The attained security levels of the resulting SUCs are evaluated and shown to be scalable and usable even for post-quantum crypto systems.

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“…(i) Efective hardware integration: WNNs are designed to readily integrate into hardware environments with constrained resources for low-power devices [15]. (ii) Swiftness and simplicity of implementation: When hardware resources are limited, WNNs are preferable since they are much simpler to build than more complicated neural network architecture.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Arora,

Naveen Venkatesh,

Sugumaran

et al. 2024

Journal of Engineering

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Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

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Design of Robust, High-Entropy Strong PUFs via Weightless Neural Network

Cited by 6 publications

References 27 publications

Securing Synchrophasors Using Data Provenance in the Quantum Era

Securing Synchrophasors Using Data Provenance in the Quantum Era

Low-Complexity Nonlinear Self-Inverse Permutation for Creating Physically Clone-Resistant Identities

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

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