The increasing integration of smart grids within the Internet of Things (IoT) ecosystem requires the implementation of robust security measures due to vulnerabilities brought about by pervasive connectivity. Previous smart grid security protocols are based on classical cryptographic algorithms, which are susceptible to significant threats from emerging quantum computers. Consequently, the adoption of quantum-resistant solutions is imperative for the long-term security of smart grids. This paper presents a novel protocol for authentication and secret key establishment in smart grids, utilizing post-quantum cryptography (PQC) algorithms. Our proposed protocol employs the FALCON digital signature algorithm for integrity verification and the CRYSTALS-Kyber key encapsulation mechanism (KEM) for secret key establishment, providing robust protection against quantum attacks. Our optimized implementation of the proposed protocol on a graphics processing unit (GPU) targets grid security module (GSM)/gateway-side deployments and demonstrates scalability under large numbers of concurrent authentication requests. Experimental validation demonstrates the proposed protocol's ability to effectively manage numerous concurrent authentication requests, ensuring secure and efficient communication within smart grid networks.
Muhammad Asfand Hafeez, Arslan Munir· ACM Transactions on Cyber-Ph...· 0 citations
Quantum machine learning is often proposed for richer feature representations, yet most evidence rests on idealized simulation rather than real noisy intermediate-scale quantum (NISQ) hardware. This research presents a controlled comparison of classical and quantum-enhanced diagnostic pipelines on three clinical binary classification tasks: Mammographic Mass, Anemia, and Diabetic Retinopathy. All pipelines share standardized preprocessing, principal component analysis (PCA), and a fixed extreme gradient boosting (XGBoost) classifier, so differences arise only from the feature representation. Four quantum encodings (angle, phase, basis, and the ZZ feature map) are each run on two backends: a noiseless simulator and the real IBM Heron r2 processor (156 qubits). Across nine performance metrics, compared with the classical pipeline, the performance decreases in the quantum hardware execution for all datasets, with a more significant reduction observed for the Anemia dataset. In contrast, compared with the simulated pipeline, the hardware execution shows a slight performance decrease for the Mammographic and Diabetic Retinopathy datasets. The exceptions are the angle and phase encodings for the Mammographic dataset, where the hardware result improves slightly compared with the simulator. For the Anemia dataset, the transition from simulation to real quantum hardware results in a considerable performance reduction. These findings show that current quantum feature maps through the encode–measure–boost pipeline on NISQ hardware do not yet outperform a well-designed classical pipeline.
Gerard Edwards, Richard Stocker, Mohammed Alharbi et al.· Electronics· 0 citations
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