4 papers
A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets
Jimmy Gammell, Kaushik Roy
Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work…
Learning to Query History: Nonstationary Classification via Learned Retrieval
Jimmy Gammell, Bishal Thapaliya, Yoon Jung +3
Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout sets available at training t…
Learning to Localize Leakage of Cryptographic Sensitive Variables
Jimmy Gammell, Anand Raghunathan, Abolfazl Hashemi +1
While cryptographic algorithms such as the ubiquitous Advanced Encryption Standard (AES) are secure, *physical implementations* of these algorithms in hardware inevitably 'leak' se…
Power side-channel leakage localization through adversarial training of deep neural networks
Jimmy Gammell, Anand Raghunathan, Kaushik Roy
Supervised deep learning has emerged as an effective tool for carrying out power side-channel attacks on cryptographic implementations. While increasingly-powerful deep learning-ba…