Side-Channel Leakage and Trace Compression using Normalized Inter-Class Variance

Security and safety critical devices must undergo penetration testing including Side-Channel Attacks (SCA) before certification. SCA are powerful and easy to mount but often need huge computation power, especially in the presence of countermeasures. Few efforts have been done to reduce the computation complexity of SCA by selecting a small subset of points where leakage prevails. In this paper, we propose a method to detect relevant leakage points in side-channel traces. The method is based on Normalized Inter-Class Variance (NICV). A key advantage of NICV over state-of-the-art is that NICV does neither need a clone device nor the knowledge of secret parameters of the crypto-system. NICV has a low computation requirement and it detects leakage using public information like input plaintexts or output ciphertexts only. It is shown that NICV can be related to Pearson correlation and signal to noise ratio (SNR) which are standard metrics. NICV can be used to theoretically compute the minimum number of traces required to attack an implementation. A theoretical rationale of NICV with some practical application on real crypto-systems are provided to support our claims

Localization of interesting points in power analysis

V současnosti je zapotřebí klást na kryptografická zařízení stále větší nároky kvůli narůstajícímu počtu zneužití. Z tohoto důvodu je nutné znát principy zabezpečení a jejich nedostatky. Tato práce se zabývá metodou lokalizace zajímavých bodů u proudové analýzy. Cílem práce je provést rozbor těchto metod a jejich následná implementace.Nowadays there are very high demands on security of cryptographic devices due to the increasing number of exploitation. Because of these reasons it is necesary to know the principles of security and their flaws. This thesis deals with the method of localization of interesting markers in current analysis. The aim of the thesis is to analyze this methods and their potential implementation in practise.

Analysis of Software Implemented Low Entropy Masking Schemes

Low Entropy Masking Schemes (LEMS) are countermeasure techniques to mitigate the high performance overhead of masked hardware and software implementations of symmetric block ciphers by reducing the entropy of the mask sets. The security of LEMS depends on the choice of the mask sets. Previous research mainly focused on searching balanced mask sets for hardware implementations. In this paper, we find that those balanced mask sets may have vulnerabilities in terms of absolute difference when applied in software implemented LEMS. The experiments verify that such vulnerabilities certainly make the software LEMS implementations insecure. To fix the vulnerabilities, we present a selection criterion to choose the mask sets. When some feasible mask sets are already picked out by certain searching algorithms, our selection criterion could be a reference factor to help decide on a more secure one for software LEMS

Side-channel leakage and trace compression using normalized inter-class variance

Security and safety critical devices must undergo penetration test-ing including Side-Channel Attacks (SCA) before certification. SCA are powerful and easy to mount but often need huge computation power, es-pecially in the presence of countermeasures. Few efforts have been done to reduce the computation complexity of SCA by selecting a small sub-set of points where leakage prevails. In this paper, we propose a method to detect relevant leakage points in side-channel traces. The method is based on Normalized Inter-Class Variance (NICV). A key advantage of NICV over state-of-the-art is that NICV does neither need a clone de-vice nor the knowledge of secret parameters of the crypto-system. NICV has a low computation requirement and it detects leakage using public information like input plaintexts or output ciphertexts only. It is shown that NICV can be related to Pearson correlation and signal to noise ratio (SNR) which are standard metrics. NICV can be used to theoretically compute the minimum number of traces required to attack an implemen-tation. A theoretical rationale of NICV with some practical application on real crypto-systems are provided to support our claims

On the Use of Independent Component Analysis to Denoise Side-Channel Measurements

International audienceIndependent Component Analysis (ICA) is a powerful technique for blind source separation. It has been successfully applied to signal processing problems, such as feature extraction and noise reduction , in many different areas including medical signal processing and telecommunication. In this work, we propose a framework to apply ICA to denoise side-channel measurements and hence to reduce the complexity of key recovery attacks. Based on several case studies, we afterwards demonstrate the overwhelming advantages of ICA with respect to the commonly used preprocessing techniques such as the singular spectrum analysis. Mainly, we target a software masked implementation of an AES and a hardware unprotected one. Our results show a significant Signal-to-Noise Ratio (SNR) gain which translates into a gain in the number of traces needed for a successful side-channel attack. This states the ICA as an important new tool for the security assessment of cryptographic implementations

High-Performance Design Patterns and File Formats for Side-Channel Analysis

Data and instruction dependent power consumption can reveal cryptographic secrets by means of Side-Channel Analysis (SCA). Consequently, manufacturers and evaluation labs perform thorough testing of cryptographic implementations to confirm their security. Unfortunately, the computation and storage needs for the resulting measurement data can be substantial and at times, limit the scope of their analyses. Therefore, it is surprising that only few publications study the efficient computation and storage of side-channel analysis related data. To address this gap, we discuss high-performance design patterns and how they align with characteristics of different file formats. More specifically, we perform an in-depth analysis of common side-channel analysis algorithms and how they can be implemented for maximum performance. At the same time, we focus on storage requirements and how to reduce them, by applying compression and chunking. In addition, we investigate and benchmark popular SCA frameworks. Moreover, we propose SCARR, a proof of concept SCA framework based on the file format Zarr, that outperforms all considered frameworks in several common algorithms (SNR, TVLA, CPA, MIA) by a factor of about two compared to the thus far fastest framework for a given profile. Most notably, in all tested scenarios, we are faster even with file compression, than other frameworks without compression. We are convinced that the presented design patterns and comparative study will benefit the greater side-channel community, help practitioners to improve their own frameworks, and reduce data storage requirements, associated costs, and lower computation/energy demands of SCA, as required to perform more testing at scale