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Wu, L. (author), Won, Yoo-Seung (author), Jap, Dirmanto (author), Perin, G. (author), Bhasin, Shivam (author), Picek, S. (author)
The use of deep learning-based side-channel analysis is an effective way of performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research articles have reported successful results, they typically focus on profiling and attacking a single device, assuming that...
journal article 2024
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