QMS: Evaluating the side-channel resistance of masked software from source code

Author(s):  
Hassan Eldib ◽  
Chao Wang ◽  
Mostafa Taha ◽  
Patrick Schaumont
10.29007/hhnf ◽  
2018 ◽  
Author(s):  
Inès Ben El Ouahma ◽  
Quentin Meunier ◽  
Karine Heydemann ◽  
Emmanuelle Encrenaz

Masking is a popular countermeasure against side-channel attacks, that randomizes secret data with random and uniform variables called masks. At software level, masking is usually added in the source code and its effectiveness needs to be verified. In this paper, we propose a symbolic method to verify side-channel robustness of masked programs. The analysis is performed at the assembly level since compilation and optimizations may alter the added protections. Our proposed method aims to verify that intermediate computations are statistically independent from secret variables using defined distribution inference rules. We verify the first round of a masked AES in 22s and show that some secure algorithms or source codes are not leakage-free in their assembly implementations.


Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Huizhong Li ◽  
Guang Yang ◽  
Jingdian Ming ◽  
Yongbin Zhou ◽  
Chengbin Jin

AbstractSide-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations. In most cases, non-linear components (e.g. S-Boxes) of cryptographic algorithms will be chosen as primary targets of side-channel attacks (SCAs). In order to measure side-channel resistance of S-Boxes, three theoretical metrics are proposed and they are reVisited transparency order (VTO), confusion coefficients variance (CCV), and minimum confusion coefficient (MCC), respectively. However, the practical effectiveness of these metrics remains still unclear. Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples, this paper takes a comprehensive study of the applicability of these metrics. First of all, we empirically investigate the relations among three metrics for targeted S-boxes, and find that CCV is almost linearly correlated with VTO, while MCC is inconsistent with the other two. Furthermore, in order to verify which metric is more effective in which scenarios, we perform simulated and practical experiments on nine 4-bit S-Boxes under the non-profiled attacks and profiled attacks, respectively. The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks, VTO and CCV are more reliable while MCC fails. We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs. Finally, we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes. Unfortunately, all of them are invalid in this scenario.


Author(s):  
Stjepan Picek ◽  
Bariş Ege ◽  
Lejla Batina ◽  
Domagoj Jakobovic ◽  
Łukasz Chmielewski ◽  
...  

Author(s):  
Matthias Gazzari ◽  
Annemarie Mattmann ◽  
Max Maass ◽  
Matthias Hollick

Wearables that constantly collect various sensor data of their users increase the chances for inferences of unintentional and sensitive information such as passwords typed on a physical keyboard. We take a thorough look at the potential of using electromyographic (EMG) data, a sensor modality which is new to the market but has lately gained attention in the context of wearables for augmented reality (AR), for a keylogging side-channel attack. Our approach is based on neural networks for a between-subject attack in a realistic scenario using the Myo Armband to collect the sensor data. In our approach, the EMG data has proven to be the most prominent source of information compared to the accelerometer and gyroscope, increasing the keystroke detection performance. For our end-to-end approach on raw data, we report a mean balanced accuracy of about 76 % for the keystroke detection and a mean top-3 key accuracy of about 32 % on 52 classes for the key identification on passwords of varying strengths. We have created an extensive dataset including more than 310 000 keystrokes recorded from 37 volunteers, which is available as open access along with the source code used to create the given results.


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