Back to Massey: Impressively fast, scalable and tight security evaluation tools

None of the existing rank estimation algorithms can scale to large cryptographic keys, such as 4096-bit (512 bytes) RSA keys. In this paper, we present the first solution to estimate the guessing entropy of arbitrarily large keys, based on mathematical bounds, resulting in the fastest and most scalable security evaluation tool to date. Our bounds can be computed within a fraction of a second, with no memory overhead, and provide a margin of only a few bits for a full 128-bit AES key

Efficient Entropy Estimation for Mutual Information Analysis Using B-Splines

International audienceThe Correlation Power Analysis (CPA) is probably the most used side-channel attack because it seems to fit the power model of most standard CMOS devices and is very efficiently computed. However, the Pearson correlation coefficient used in the CPA measures only linear statistical dependences where the Mutual Information (MI) takes into account both linear and nonlinear dependences. Even if there can be simultaneously large correlation coefficients quantified by the correlation coefficient and weak dependences quantified by the MI, we can expect to get a more profound understanding about interactions from an MI Analysis (MIA). We study methods that improve the non-parametric Probability Density Functions (PDF) in the estimation of the entropies and, in particular, the use of B-spline basis functions as pdf estimators. Our results indicate an improvement of two fold in the number of required samples compared to a classic MI estimation. The B-spline smoothing technique can also be applied to the rencently introduced Cramér-von-Mises test

Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose ESrank, the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity, which also achieves excellent practical performance. Our main idea is to use exponential sampling to drastically reduce the algorithm\u27s complexity. Importantly, ESrank is simple to build from scratch, and requires no algorithmic tools beyond a sorting function. After rigorously bounding the accuracy, time and space complexities, we evaluated the performance of ESrank on a real SCA data corpus, and compared it to the currently-best histogram-based algorithm. We show that ESrank gives excellent rank estimation (with roughly a 1-bit margin between lower and upper bounds), with a performance that is on-par with the Histogram algorithm: a run-time of under 1 second on a standard laptop using 6.5 MB RAM

Horizontal Side-Channel Attacks and Countermeasures on the ISW Masking Scheme

International audienceA common countermeasure against side-channel attacks consists in using the masking scheme originally introduced by Ishai, Sahai and Wagner (ISW) at Crypto 2003, and further generalized by Rivain and Prouff at CHES 2010. The countermeasure is provably secure in the probing model, and it was showed by Duc, Dziembowski and Faust at Eurocrypt 2014 that the proof can be extended to the more realistic noisy leakage model. However the extension only applies if the leakage noise σ increases at least linearly with the masking order n, which is not necessarily possible in practice. In this paper we investigate the security of an implementation when the previous condition is not satisfied, for example when the masking order n increases for a constant noise σ. We exhibit two (template) horizontal side-channel attacks against the Rivain-Prouff's secure multiplication scheme and we analyze their efficiency thanks to several simulations and experiments. Eventually, we describe a variant of Rivain-Prouff's multiplication that is still provably secure in the original ISW model, and also heuristically secure against our new attacks

From Improved Leakage Detection to the Detection of Points of Interests in Leakage Traces

Leakage detection usually refers to the task of identifying data-dependent information in side-channel measurements, independent of whether this information can be exploited. Detecting Points-Of-Interest (POIs) in leakage traces is a complementary task that is a necessary first step in most side-channel attacks, where the adversary wants to turn this information into (e.g.) a key recovery. In this paper, we discuss the differences between these tasks, by investigating a popular solution to leakage detection based on a t-test, and an alternative method exploiting Pearson\u27s correlation coefficient. We first show that the simpler t-test has better sampling complexity, and that its gain over the correlation-based test can be predicted by looking at the Signal-to-Noise Ratio (SNR) of the leakage partitions used in these tests. This implies that the sampling complexity of both tests relates more to their implicit leakage assumptions than to the actual statistics exploited. We also put forward that this gain comes at the cost of some intuition loss regarding the localization of the exploitable leakage samples in the traces, and their informativeness. Next, and more importantly, we highlight that our reasoning based on the SNR allows defining an improved t-test with significantly faster detection speed (with approximately 5 times less measurements in our experiments), which is therefore highly relevant for evaluation laboratories. We finally conclude that whereas t-tests are the method of choice for leakage detection only, correlation-based tests exploiting larger partitions are preferable for detecting POIs. We confirm this intuition by improving automated tools for the detection of POIs in the leakage measurements of a masked implementation, in a black box manner and without key knowledge, thanks to a correlation-based leakage detection test

On the Power of Fault Sensitivity Analysis and Collision Side-Channel Attacks in a Combined Setting

Abstract. At CHES 2010 two powerful new attacks were presented, namely the Fault Sensitivity Analysis and the Correlation Collision At-tack. This paper shows how these ideas can be combined to create even stronger attacks. Two solutions are presented; both extract leakage infor-mation by the fault sensitivity analysis method while each one applies a slightly different collision attack to deduce the secret information without the need of any hypothetical leakage model. Having a similar fault injec-tion method, one attack utilizes the non-uniform distribution of faulty ciphertext bytes while the other one exploits the data-dependent timing characteristics of the target combination circuit. The results when at-tacking several AES ASIC cores of the SASEBO LSI chips in different process technologies are presented. Successfully breaking the cores pro-tected against DPA attacks using either gate-level countermeasures or logic styles indicates the strength of the attacks.

Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures.

International audienceIn the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks

Single-Trace Side-Channel Attacks on Masked Lattice-Based Encryption

Although lattice-based cryptography has proven to be a particularly efficient approach to post-quantum cryptography, its security against side-channel attacks is still a very open topic. There already exist some first works that use masking to achieve DPA security. However, for public-key primitives SPA attacks that use just a single trace are also highly relevant. For lattice-based cryptography this implementation-security aspect is still unexplored. In this work, we present the first single-trace attack on lattice-based encryption. As only a single side-channel observation is needed for full key recovery, it can also be used to attack masked implementations. We use leakage coming from the Number Theoretic Transform, which is at the heart of almost all efficient lattice-based implementations. This means that our attack can be adapted to a large range of other lattice-based constructions and their respective implementations. Our attack consists of 3 main steps. First, we perform a template matching on all modular operations in the decryption process. Second, we efficiently combine all this side-channel information using belief propagation. And third, we perform a lattice-decoding to recover the private key. We show that the attack allows full key recovery not only in a generic noisy Hamming-weight setting, but also based on real traces measured on an ARM Cortex-M4F microcontroller