LD-PA: Distilling Univariate Leakage for Deep Learning-Based Profiling Attacks

IEEE Transactions on Information Forensics and Security, vol. 20, pp. 17–30

Abstract

The deep learning-based profiling attacks have received significant attention for their potential against masking-protected devices. Currently, additional capabilities like exploiting only a segment of the side-channel traces or having knowledge of the specific countermeasure scheme have been granted to attackers during the profiling phase. In case either capability is removed, a practical profiling attack faces great difficulty and complexity. To address this challenge, we propose an efficient and scheme-agnostic Leakage Distillation-based Profiling Attack (LD-PA). By distilling univariate leakage from a reference, we can train an encoder that extracts multivariate leakage from raw traces and transforms it into an effective representation (transitional leakage). An indirect connection between multivariate leakage and the target variable is established by bridging through the transitional leakage, thereby facilitating the inference of leaked values. Remarkably, LD-PA achieves successful attacks on multiple public datasets using a simple multilayer perceptron (MLP) without necessitating an exhaustive hyperparameter search, while its performance is competitive with state-of-the-art methods. Simultaneously, we delve into the nature of transitional leakage, confirming the existence of combined leakage. This, in turn, validates that the guidance from univariate leakage references aids in the combination of multivariate leakage. Besides that, each component of the multivariate leakage is extracted and stacked in a highly aligned manner. Moreover, we explored several factors impacting LD-PA performance, covering scenarios with limited profiling traces, noisy references, alternative references, and hyperparameter tuning.

Authors 4

1. Changcheng Xiao

   Wuhan University

   Affiliation as printed

   Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

   Ministry of Education, School of Cyber Science and Engineering, Key Laboratory of Aerospace Information Security and Trusted Computing, Wuhan University, Wuhan, China

2. Ming Tang

   Wuhan University

   Affiliation as printed

   Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

   Ministry of Education, School of Cyber Science and Engineering, Key Laboratory of Aerospace Information Security and Trusted Computing, Wuhan University, Wuhan, China

3. Sengim Karayalçın Aachen Leiden Institute of AdvancedComputer Science (LACS)

   Leiden University

   Affiliation as printed

   Leiden Institute of AdvancedComputer Science (LACS), Leiden University, Leiden, EZ, The Netherlands

   Leiden University, Leiden, The Netherlands

4. Wei Cheng

   Institut Polytechnique de Paris · Laboratoire Traitement et Communication de l’Information

   Affiliation as printed

   LTCI Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France

   LTCI, Télécom Paris, Institut Polytechnique de Paris; Secure-IC S.A.S., Paris, France