Benedikt Gierlichs

Bio: Benedikt Gierlichs is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Side channel attack & Fault (power engineering). The author has an hindex of 34, co-authored 94 publications receiving 4226 citations. Previous affiliations of Benedikt Gierlichs include iMinds.

Mutual Information Analysis

Abstract: We propose a generic information-theoretic distinguisher for differential side-channel analysis. Our model of side-channel leakage is a refinement of the one given by Standaert et al.An embedded device containing a secret key is modeled as a black box with a leakage function whose output is captured by an adversary through the noisy measurement of a physical observable. Although quite general, the model and the distinguisher are practical and allow us to develop a new differential side-channel attack. More precisely, we build a distinguisher that uses the value of the Mutual Information between the observed measurements and a hypothetical leakage to rank key guesses. The attack is effective without any knowledge about the particular dependencies between measurements and leakage as well as between leakage and processed data, which makes it a universal tool. Our approach is confirmed by results of power analysis experiments. We demonstrate that the model and the attack work effectively in an attack scenario against DPA-resistant logic.

The World is Not Enough: Another Look on Second-Order DPA

Abstract: In a recent work, Mangard et al. showed that under certain assumptions, the (so-called) standard univariate side-channel attacks using a distance-of-means test, correlation analysis and Gaussian templates are essentially equivalent. In this paper, we show that in the context of multivariate attacks against masked implementations, this conclusion does not hold anymore. While a single distinguisher can be used to compare the susceptibility of different unprotected devices to first-order DPA, understanding second-order attacks requires to carefully investigate the information leakages and the adversaries exploiting these leakages, separately. Using a framework put forward by Standaert et al. at Eurocrypt 2009, we provide the first analysis that explores these two topics in the case of a masked implementation exhibiting a Hamming weight leakage model. Our results lead to refined intuitions regarding the efficiency of various practically-relevant distinguishers. Further, we also investigate the case of second- and third-order masking (i.e. using three and four shares to represent one value). This evaluation confirms that higher-order masking only leads to significant security improvements if the secret sharing is combined with a sufficient amount of noise. Eventually, we show that an information theoretic analysis allows determining this necessary noise level, for different masking schemes and target security levels, with high accuracy and smaller data complexity than previous methods.

Machine learning in side-channel analysis: a first study

Abstract: Electronic devices may undergo attacks going beyond traditional cryptanalysis. Side-channel analysis (SCA) is an alternative attack that exploits information leaking from physical implementations of e.g. cryptographic devices to discover cryptographic keys or other secrets. This work comprehensively investigates the application of a machine learning technique in SCA. The considered technique is a powerful kernel-based learning algorithm: the Least Squares Support Vector Machine (LS-SVM). The chosen side-channel is the power consumption and the target is a software implementation of the Advanced Encryption Standard. In this study, the LS-SVM technique is compared to Template Attacks. The results show that the choice of parameters of the machine learning technique strongly impacts the performance of the classification. In contrast, the number of power traces and time instants does not influence the results in the same proportion. This effect can be attributed to the usage of data sets with straightforward Hamming weight leakages in this first study.

Mutual Information Analysis: a Comprehensive Study

Abstract: Mutual Information Analysis is a generic side-channel distinguisher that has been introduced at CHES 2008. It aims to allow successful attacks requiring minimum assumptions and knowledge of the target device by the adversary. In this paper, we compile recent contributions and applications of MIA in a comprehensive study. From a theoretical point of view, we carefully discuss its statistical properties and relationship with probability density estimation tools. From a practical point of view, we apply MIA in two of the most investigated contexts for side-channel attacks. Namely, we consider first-order attacks against an unprotected implementation of the DES in a full custom IC and second-order attacks against a masked implementation of the DES in an 8-bit microcontroller. These experiments allow to put forward the strengths and weaknesses of this new distinguisher and to compare it with standard power analysis attacks using the correlation coefficient.

Mutual Information Analysis A Generic Side-Channel Distinguisher

Abstract: We propose a generic information-theoretic distinguisher for differential side-channel analysis. Our model of side-channel leakage is a refinement of the one given by Standaert et al. An embedded device containing a secret key is modeled as a black box with a leakage function whose output is captured by an adversary through the noisy measure- ment of a physical observable. Although quite general, the model and the distinguisher are practical and allow us to develop a new differen- tial side-channel attack. More precisely, we build a distinguisher that uses the value of the Mutual Information between the observed measurements and a hypothetical leakage to rank key guesses. The attack is effective without any knowledge about the particular dependencies between mea- surements and leakage as well as between leakage and processed data, which makes it a universal tool. Our approach is confirmed by results of power analysis experiments. We demonstrate that the model and the attack work effectively in an attack scenario against DPA-resistant logic.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks

Abstract: The fair evaluation and comparison of side-channel attacks and countermeasures has been a long standing open question, limiting further developments in the field. Motivated by this challenge, this work makes a step in this direction and proposes a framework for the analysis of cryptographic implementations that includes a theoretical model and an application methodology. The model is based on commonly accepted hypotheses about side-channels that computations give rise to. It allows quantifying the effect of practically relevant leakage functions with a combination of information theoretic and security metrics, measuring the quality of an implementation and the strength of an adversary, respectively. From a theoretical point of view, we demonstrate formal connections between these metrics and discuss their intuitive meaning. From a practical point of view, the model implies a unified methodology for the analysis of side-channel key recovery attacks. The proposed solution allows getting rid of most of the subjective parameters that were limiting previous specialized and often ad hoc approaches in the evaluation of physically observable devices. It typically determines the extent to which basic (but practically essential) questions such as "How to compare two implementations? " or "How to compare two side-channel adversaries? " can be answered in a sound fashion.