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A machine learning low-dropout regulator-assisted differential power analysis attack countermeasure with voltage scaling.

, , , , , , , , , , and . Int. J. Circuit Theory Appl., 51 (7): 3105-3117 (July 2023)

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A machine learning low-dropout regulator-assisted differential power analysis attack countermeasure with voltage scaling., , , , , , , , , and 1 other author(s). Int. J. Circuit Theory Appl., 51 (7): 3105-3117 (July 2023)Comprehensive Designs of Innovate Secure Hardware Devices against Machine Learning Attacks and Power Analysis Attacks.. Old Dominion University, Norfolk, Virginia, USA, (2020)base-search.net (ftolddominionuni:oai:digitalcommons.odu.edu:ece_etds-1210).Personalized learning efficiency data analysis based on multi-scale convolution architecture and hybrid loss., , and . Neural Comput. Appl., 36 (17): 9753-9766 (2024)A novel SM4 cryptographic architecture against higher order power analysis attacks., , , , , , , , , and 7 other author(s). Int. J. Circuit Theory Appl., 52 (8): 4184-4198 (August 2024)Leakage Power Analysis (LPA) Attack in Breakdown Mode and Countermeasure., and . SoCC, page 102-105. IEEE, (2018)A Novel PUF Architecture Against Non-Invasive Attacks., , and . SLIP, page 1-5. IEEE, (2019)Convolutional Neural Networks (CNNs)-Assisted Voltage Regulation: A New Power Delivery Scheme., and . NATW, page 1-6. IEEE, (2019)Combining Thermal Maps With Inception Neural Networks for Hardware Trojan Detection., and . IEEE Embed. Syst. Lett., 13 (2): 45-48 (2021)Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model., , , , , , and . Int. J. Neural Syst., 34 (3): 2450012:1-2450012:16 (March 2024)Leveraging Balanced Logic Gates as Strong PUFs for Securing IoT Against Malicious Attacks., and . J. Electron. Test., 35 (6): 853-865 (2019)