LEADER 03876nam 22007095 450 001 9910627259803321 005 20251113185652.0 010 $a981-19-4017-7 024 7 $a10.1007/978-981-19-4017-0 035 $a(MiAaPQ)EBC7088008 035 $a(Au-PeEL)EBL7088008 035 $a(CKB)24837145900041 035 $a(PPN)264953673 035 $a(OCoLC)1345585543 035 $a(DE-He213)978-981-19-4017-0 035 $a(EXLCZ)9924837145900041 100 $a20220915d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning for Computational Problems in Hardware Security $eModeling Attacks on Strong Physically Unclonable Function Circuits /$fby Pranesh Santikellur, Rajat Subhra Chakraborty 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (92 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1052 311 08$aPrint version: Santikellur, Pranesh Deep Learning for Computational Problems in Hardware Security Singapore : Springer,c2022 9789811940163 320 $aIncludes bibliographical references. 327 $aChapter 1: Introduction -- Chapter 2: Fundamental Concepts of Machine Learning -- Chapter 3: Supervised Machine Learning Algorithms for PUF Modeling Attacks -- Chapter 4: Deep Learning based PUF Modeling Attacks -- Chapter 5: Tensor Regression based PUF Modeling Attack -- Chapter 6: Binarized Neural Network based PUF Modeling -- Chapter 7: Conclusions and Future Work. . 330 $aThe book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v1052 606 $aElectronic circuits 606 $aArtificial intelligence 606 $aMathematics 606 $aComputers, Special purpose 606 $aComputer science 606 $aElectronic Circuits and Systems 606 $aArtificial Intelligence 606 $aMathematics in Popular Science 606 $aSpecial Purpose and Application-Based Systems 606 $aComputer Science 615 0$aElectronic circuits. 615 0$aArtificial intelligence. 615 0$aMathematics. 615 0$aComputers, Special purpose. 615 0$aComputer science. 615 14$aElectronic Circuits and Systems. 615 24$aArtificial Intelligence. 615 24$aMathematics in Popular Science. 615 24$aSpecial Purpose and Application-Based Systems. 615 24$aComputer Science. 676 $a006.3 700 $aSantikellur$b Pranesh$01267562 702 $aChakraborty$b Rajat Subhra 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910627259803321 996 $aDeep learning for computational problems in hardware security$93010766 997 $aUNINA