MARPUF: physical unclonable function with improved machine learning attack resistance

Abstract

Nowadays, physical unclonable functions (PUFs) are emerging as one of the key building blocks for device authentication and key generation. Although PUF is very useful in the area of hardware security, it is vulnerable to machine learning modelling attacks (ML-MA) by modelling the challenge-response pairs (CRPs) behaviour. To this end, this study proposes a novel PUF named MARPUF, which gives good resistance to machine learning (ML) attacks. The study proposed a MARPUF design, where the mapping of CRPs is randomized by implementing two-round challenges to meet the randomness requirements for ML resistance. Ssome of the popular ML techniques are used to test the ML attack resistance and compare the results with some existing PUFs. We evaluate the performance of the PUF against various parameters like reliability, uniformity, uniqueness, etc. The hardware cost analysis shows that MARPUF requires lesser hardware than the existing ML-MA resistant PUFs. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

“…Generalized Neuro-Fuzzy Models (GNFM) [ 60 ], ANN model [ 61 ], ANFIS model [ 62 ], MLP-NN [ 63 ] [ 113 ], ELM algorithm [ 64 ] [ 90 ], M5 Model Tree [ 113 ], LS-SVR , MARS, ELM, WNN and GANN [ 66 ]. The DL model and ML model are applied in various domains such the COVID-19 analysis [ 114 ], proposed a novel method based on artificial intelligence (AI) to identify COVID-19 disease [ 115 ], developed genetically optimized Deep Neural Network [ 116 ], Tripathy et al [ 117 ] investigated the performance of MARPUF approach and it is found to be better resistant to such modelling attacks, image classification using deep learning [ 118 ], Artificial Intelligence approaches used to classifying various types of cancer [ 119 ], enhanced the grip functionality of myoelectric hands based on deep learning [ 120 ], and classifiers for on-line handwriting recognition based on SVM and KNN algorithms [ 121 ], and a survey for software fault prediction [ 122 ]. Singh et al [ 123 ] presented the efficient results and reliable algorithm for optimal design of water distribution networks.The literature summary of ML and EA are given in Table 5 .…”