N
Nasour Bagheri
Researcher at Shahid Rajaee Teacher Training University
Publications - 152
Citations - 1339
Nasour Bagheri is an academic researcher from Shahid Rajaee Teacher Training University. The author has contributed to research in topics: Authentication protocol & Block cipher. The author has an hindex of 18, co-authored 137 publications receiving 1035 citations. Previous affiliations of Nasour Bagheri include Iran University of Science and Technology.
Papers
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Journal ArticleDOI
New differential fault analysis on PRESENT
TL;DR: Two differential fault analyses on PRESENT-80, a lightweight block cipher, are presented and the last subkey of the block cipher can be retrieved, given 48 faulty cipher texts on average.
Book ChapterDOI
Cryptanalysis of SIMON Variants with Connections
Javad Alizadeh,Hoda AlKhzaimi,Mohammad Reza Aref,Nasour Bagheri,Praveen Gauravaram,Abhishek Kumar,Martin M. Lauridsen,Somitra Kumar Sanadhya +7 more
TL;DR: This work presents several linear characteristics for reduced-round SIMON32/64 that can be used for a key-recovery attack and extend them further to attack other variants of SIMON, and exploits a connection between linear and differential characteristics for SIMON to constructlinear characteristics for different variants of reduced- round SIMON.
Journal ArticleDOI
Passive secret disclosure attack on an ultralightweight authentication protocol for Internet of Things
Masoumeh Safkhani,Nasour Bagheri +1 more
TL;DR: The security of the proposed ultralightweight RFID authentication protocol is considered and a passive secret disclosure attack against it is presented and it is verified the correctness of the presented attack by simulation.
Journal ArticleDOI
Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models.
TL;DR: It is observed that humans relied on specific (diagnostic) object regions for accurate recognition which remained relatively consistent across variations; but feed-forward feature-extraction models selected view-specific (non-invariant) features across variations, suggesting that models can develop different strategies, but reach human-level recognition performance.
Book ChapterDOI
Linear Cryptanalysis of Reduced-Round SIMECK Variants
TL;DR: In this paper, the authors showed that the security of SIMECK is not as good as its predecessors SIMON and SPECK, and presented a linear attack in this senario.