Leakage Detection with Kolmogorov-Smirnov Test

Leakage detection seeking the evidence of sensitive data dependencies in the side-channel traces instead of trying to recover the sensitive data directly under the enormous efforts with numerous leakage models and state-of-the-art distinguishers can provide a fast preliminary security assessment on the cryptographic devices for designers and evaluators. Therefore, it is a popular topic in recent side-channel research of which the Welch\u27s $t$-test-based Test Vector Leakage Assessment (TVLA) methodology is the most widely used one. However, the TVLA is not always the best option under all kinds of conditions (as we can see in the latter section of this paper). Kolmogorov-Smirnov test is a well-known nonparametric method for statistical analysis to determine whether the samples are from the same distribution by analyzing the cumulative distribution. It has been proposed into side-channel analysis as a successful distinguisher. This paper proposes---to our knowledge, for the first time---Kolmogorov-Smirnov test as a new method for leakage detection. Besides, we propose two implementations to speed up the KS leakage detection procedure. Experimental results on simulated leakage with various parameters and the practical traces verify that KS is an effective and robust leakage detection tool and the comprehensive comparison with TVLA shows that KS-based leakage detection can be a right-hand supplement to TVLA when performing the side-channel assessment

On the susceptibility of Texas Instruments SimpleLink platform microcontrollers to non-invasive physical attacks

We investigate the susceptibility of the Texas Instruments SimpleLink platform microcontrollers to non-invasive physical attacks. We extracted the ROM bootloader of these microcontrollers and then analysed it using static analysis augmented with information obtained through emulation. We demonstrate a voltage fault injection attack targeting the ROM bootloader that allows to enable debug access on a previously locked microcontroller within seconds. Information provided by Texas Instruments reveals that one of our voltage fault injection attacks abuses functionality that is left over from the integrated circuit manufacturing process. The demonstrated physical attack allows an adversary to extract the firmware (i.e. intellectual property) and to bypass secure boot. Additionally, we mount side-channel attacks and differential fault analysis attacks on the hardware AES co-processor. To demonstrate the practical applicability of these attacks we extract the firmware from a Tesla Model 3 key fob. This paper describes a case study covering Texas Instruments SimpleLink microcontrollers. Similar attack techniques can be, and have been, applied to microcontrollers from other manufacturers. The goal of our work is to document our analysis methodology and to ensure that system designers are aware of these vulnerabilities. They will then be able to take these into account during the product design phase. All identified vulnerabilities were responsibly disclosed

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

Autoencoder Assist: An Efficient Profiling Attack on High-dimensional Datasets

Deep learning (DL)-based profiled attack has been proved to be a powerful tool in side-channel analysis. A variety of multi-layer perception (MLP) networks and convolutional neural networks (CNN) are thereby applied to cryptographic algorithm implementations for exploiting correct keys with a smaller number of traces and a shorter time. However, most attacks merely focus on small datasets, in which their points of interest are well-trimmed for attacks. Countermeasures applied in embedded systems always result in high-dimensional side-channel traces, i.e., the high-dimension of each input trace. Time jittering and random delay techniques introduce desynchronization but increase SCA complexity as well. These traces inevitably require complicated designs of neural networks and large sizes of trainable parameters for exploiting the correct keys. Therefore, performing profiled attacks (directly) on high-dimensional datasets is difficult. To bridge this gap, we propose a dimension reduction tool for high-dimensional traces by combining signal-to-noise ratio (SNR) analysis and autoencoder. With the designed asymmetric undercomplete autoencoder (UAE) architecture, we extract a small group of critical features from numerous time samples. The compression rate by using our UAE method reaches 40x on synchronized datasets and 30x on desynchronized datasets. This preprocessing step facilitates the profiled attacks by extracting potential leakage features. To demonstrate its effectiveness, we evaluate our proposed method on the raw ASCAD dataset with 100,000 samples in each trace. We also derive desynchronized datasets from the raw ASCAD dataset and validate our method under random delay effect. We further propose a $2^n$-structure MLP network as the attack model. By applying UAE and 2^n-structure MLP network on these traces, experimental results show that all correct subkeys on synchronized datasets (16 S-boxes) and desynchronized datasets are successfully revealed within hundreds of seconds. This shows that our autoencoder can significantly facilitate DL-based profiled attacks on high-dimensional datasets

Assessment of attribute-based credentials for privacy-preserving road traffic services in smart cities

Smart cities involve the provision of advanced services for road traffic users. Vehicular ad hoc networks (VANETs) are a promising communication technology in this regard. Preservation of privacy is crucial in these services to foster their acceptance. Previous approaches have mainly focused on PKI-based or ID-based cryptography. However, these works have not fully addressed the minimum information disclosure principle. Thus, questions such as how to prove that a driver is a neighbour of a given zone, without actually disclosing his identity or real address, remain unaddressed. A set of techniques, referred to as Attribute-Based Credentials (ABCs), have been proposed to address this need in traditional computation scenarios. In this paper, we explore the use of ABCs in the vehicular context. For this purpose, we focus on a set of use cases from European Telecommunications Standards Institute (ETSI) Basic Set of Applications, specially appropriate for the early development of smart cities. We assess which ABC techniques are suitable for this scenario, focusing on three representative ones—Idemix, U-Prove and VANET-updated Persiano systems. Our experimental results show that they are feasible in VANETs considering state-of-the-art technologies, and that Idemix is the most promising technique for most of the considered use cases.This work was supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You); the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks) and by the MINECO grant TIN2016-79095-C2-2-R (SMOG-DEV - Security mechanisms for fog computing: advanced security for devices). Jose Maria de Fuentes and Lorena Gonzalez were also supported by the Programa de Ayudas para la Movilidad of Carlos III University of Madrid

DLPFA: Deep Learning based Persistent Fault Analysis against Block Ciphers

Deep learning techniques have been widely applied to side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning techniques into fault analysis to perform key recovery. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provide outstanding performance with a suitable selection of hyper-parameters

Optimal First-Order Boolean Masking for Embedded IoT Devices

Boolean masking is an effective side-channel countermeasure that consists in splitting each sensitive variable into two or more shares which are carefully manipulated to avoid leakage of the sensitive variable. The best known expressions for Boolean masking of bitwise operations are relatively compact, but even a small improvement of these expressions can significantly reduce the performance penalty of more complex masked operations such as modular addition on Boolean shares or of masked ciphers. In this paper, we present and evaluate new secure expressions for performing bitwise operations on Boolean shares. To this end, we describe an algorithm for efficient search of expressions that have an optimal cost in number of elementary operations. We show that bitwise AND and OR on Boolean shares can be performed using less instructions than the best known expressions. More importantly, our expressions do no require additional random values as the best known expressions do. We apply our new expressions to the masked addition/subtraction on Boolean shares based on the Kogge-Stone adder and we report an improvement of the execution time between 14% and 19%. Then, we compare the efficiency of first-order masked implementations of three lightweight block ciphers on an ARM Cortex-M3 to determine which design strategies are most suitable for efficient masking. All our masked implementations passed the t-test evaluation and thus are deemed secure against first-order side-channel attacks

Security of Ubiquitous Computing Systems

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license

Classifiers of power patterns

V průběhu posledních několika let se z útoků postranními kanály stala významná hrozba pro bezpečnost kryptografických modulů. Existuje několik typů útoků postranními kanály, které lze použít pro prolomení většiny šifrovacích algoritmů (např. AES, DES, RSA). Tato diplomová práce se věnuje problematice proudových postranních kanálů, pro které existují různé metody proudové analýzy, např. jednoduchá proudová analýza (SPA), diferenciální proudová analýza (DPA), útok pomocí šablon, atd. Výše zmíněné metody jsou v práci podrobně popsány. Také je zde zkoumáno uplatnění technik strojového učení, konkrétně neuronových sítí a algoritmu SVM, v oblasti proudové analýzy. Praktická část práce se zaměřuje na prolomení maskovaného šifrovacího algoritmu AES. Jehož implementace je použita v soutěži DPA Contest.Over the last several years side-channel analysis has emerged as a major threat to securing sensitive information in cryptographic devices. Several side-channels have been discovered and used to break implementations of all major cryptographic algorithms (AES, DES, RSA). This thesis is focused on power analysis attacks. A variety of power analysis methods has been developed to perform these attacks. These methods include simple power analysis (SPA), differential power analysis (DPA), template attacks, etc. This work provides comprehensive survey of mentioned methods and also investigates the application of a machine learning techniques in power analysis. The considered learning techniques are neural networks and support vector machines. The final part of this thesis is dedicated to implemenation of the attack against protected software AES implementation which is used in the DPA Contest.

Security of Ubiquitous Computing Systems

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license