On the Instability of Softmax Attention-Based Deep Learning Models in Side-Channel Analysis-Reference-Cited by-同舟云学术

On the Instability of Softmax Attention-Based Deep Learning Models in Side-Channel Analysis

Published:2024 Issue: Volume:19 Page:514-528
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Hajra Suvadeep¹^ORCID,Alam Manaar²^ORCID,Saha Sayandeep³^ORCID,Picek Stjepan⁴^ORCID,Mukhopadhyay Debdeep¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

2. Center for Cyber Security, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

3. UCL Crypto Group, ICTEAM/ELEN, Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium

4. Digital Security Group, Radboud University, Nijmegen, The Netherlands

Funder

Prime Minister’s Research Fellowship, India

Framework of Nationale Wetenschaps Agenda (NWA) Cybersecurity Call with Project name PROACT, through the Netherlands Organization for Scientific Research

NWO Project DISTANT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10290992.pdf?arnumber=10290992

Reference24 articles.

1. Neural machine translation by jointly learning to align and translate;bahdanau;Proc ICLR,2015

2. Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree;lee;Proc 19th Int Conf Artif Intell Stat,2016

3. Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures

4. Visualizing the loss landscape of neural nets;li;arXiv 1712 09913,2017

5. Attention is all you need;vaswani;Proc NIPS,2017