Searched for: author%3A%22Bhasin%2C+Shivam%22
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document
Batina, Lejla (author), Bhasin, Shivam (author), Jap, Dirmanto (author), Picek, S. (author)
This paper was selected for Top Picks in Hardware and Embedded Security 2020 and it presents a physical side-channel attack aiming at reverse engineering neural networks implemented on an edge device. The attack does not need access to training data and allows for neural network recovery by feeding known random inputs. We successfully reverse...
journal article 2022
document
Batina, Lejla (author), Jap, Dirmanto (author), Bhasin, Shivam (author), Picek, S. (author)
Machine learning has become mainstream across industries. Numerous examples prove the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using side-channel information such as timing and electromagnetic (EM) emanations. To this end, we consider multilayer perceptron and...
conference paper 2019
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Kim, Jaehun (author), Picek, S. (author), Heuser, Annelie (author), Bhasin, Shivam (author), Hanjalic, A. (author)
Profiled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new...
journal article 2019
document
Picek, S. (author), Heuser, Annelie (author), Jovic, Alan (author), Bhasin, Shivam (author), Regazzoni, Francesco (author)
We concentrate on machine learning techniques used for profiled side-channel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of...
journal article 2018
Searched for: author%3A%22Bhasin%2C+Shivam%22
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