Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGonella, Vijayakanthi-
dc.contributor.authorMohanty, Jaganath Prasad-
dc.contributor.authorSwain, Ayas Kanta-
dc.contributor.authorMahapatra, Kamalakanta-
dc.identifier.citationIEEE-iSES 2021, MNIT Jaipur, India,20-22 Dec2021en_US
dc.descriptionCopyright of this paper is with proceedings publisheren_US
dc.description.abstractPower Side-Channel analysis recovers sensitive information not only from physical proximity to a device but also from basic knowledge of sample leaked data collection. With minimum mean squared error metric, power analysis using a deep learning test case increases the confidence level of proper identification of leaked data. Comparison with state-of-the-art technology in this work shows improved performance in the non-profiled SCA category of detection. The deep learning technique aids in calculating the average loss gradient values and the loss values, both being calculated by taking the traces in MathWorks implementation as the training data and the MSB values of the intermediate values as the training labels to reveal the expected secret key. Moreover, iterative training of some machine learning techniques with different FPGA boards implementing cryptographic designs increased the accuracy of leakage detection at an earlier stage to a better extent.en_US
dc.subjectSide Channel Analysisen_US
dc.subjectMachine Learning; Lossen_US
dc.subjectGradient; Multi-Layer Perceptron;en_US
dc.subjectAES Cryptographyen_US
dc.titleDifferential Metric Based Deep Learning Methodology for Non-Profiled Side Channel Analysisen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
Swain AK_IEEE-iSES 2021.pdf308.15 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.