A Bounded-Space Near-Optimal Key Enumeration Algorithm for Multi-Dimensional Side-Channel Attacks

Enumeration of cryptographic keys in order of likelihood based on side-channel leakages has a significant importance in cryptanalysis. Previous algorithms enumerate the keys in optimal order, however their space complexity is $\Omega(n^{d/2})$ when there are d subkeys and n candidate values per subkey. We propose a new key enumeration algorithm that has a space complexity bounded by $O(d^2 w+dn)$, when w is a design parameter, which allows the enumeration of many more keys without exceeding the available space. The trade-off is that the enumeration order is only near-optimal, with a bounded ratio between optimal and near-optimal ranks. Before presenting our algorithm we provide bounds on the guessing entropy of the full key in terms of the easy-to-compute guessing entropies of the individual subkeys. We use these results to quantify the near-optimality of our algorithm\u27s ranking, and to bound its guessing entropy. We evaluated our algorithm through extensive simulations. We show that our algorithm continues its near-optimal-order enumeration far beyond the rank at which the optimal algorithm fails due to insufficient memory, on realistic SCA scenarios. Our simulations utilize a new model of the true rank distribution, based on long tail Pareto distributions, that is validated by empirical data and may be of independent interest

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

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

PRank: Fast Analytical Rank Estimation via Pareto Distributions

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 a new method called PRank for rank estimation, that is conceptually simple, and more time and memory efficient than previous proposals. Our main idea is to bound each subkey distribution by a Pareto-like function: since these are analytical functions, we can then estimate the rank by a closed formula. We evaluated the performance of PRank through extensive simulations based on two real SCA data corpora, and compared it to the currently-best histogram-based algorithm. We show that PRank gives a good rank estimation with much improved time and memory efficiency, especially for large ranks: For ranks between $2^{80}-2^{100}$ PRank estimation is at most 10 bits above the histogram rank and for ranks beyond $2^{100}$ the PRank estimation is only 4 bits above the histogram rank---yet it runs faster, and uses negligible memory. PRank gives a new and interesting method to solve the rank estimation problem based on reduction to analytical functions and calculating one closed formula hence using negligible time and space

Simple Key Enumeration (and Rank Estimation) using Histograms: an Integrated Approach

The main contribution of this paper, is a new key enumeration algorithm that combines the conceptual simplicity of the rank estimation algorithm of Glowacz et al. (from FSE 2015) and the parallelizability of the enumeration algorithm of Bogdanov et al. (SAC 2015) and Martin et al. (from ASIACRYPT 2015). Our new algorithm is based on histograms. It allows obtaining simple bounds on the (small) rounding errors that it introduces and leads to straightforward parallelization. We further show that it can minimize the bandwidth of distributed key testing by selecting parameters that maximize the factorization of the lists of key candidates produced by the enumeration, which can be highly beneficial, e.g. if these tests are performed by a hardware coprocessor. We also put forward that the conceptual simplicity of our algorithm translates into efficient implementations (that slightly improve the state-of-the-art). As an additional consolidating effort, we finally describe an open source implementation of this new enumeration algorithm, combined with the FSE 2015 rank estimation one, that we make available with the paper

Side Channel Leakage Analysis - Detection, Exploitation and Quantification

Nearly twenty years ago the discovery of side channel attacks has warned the world that security is more than just a mathematical problem. Serious considerations need to be placed on the implementation and its physical media. Nowadays the ever-growing ubiquitous computing calls for in-pace development of security solutions. Although the physical security has attracted increasing public attention, side channel security remains as a problem that is far from being completely solved. An important problem is how much expertise is required by a side channel adversary. The essential interest is to explore whether detailed knowledge about implementation and leakage model are indispensable for a successful side channel attack. If such knowledge is not a prerequisite, attacks can be mounted by even inexperienced adversaries. Hence the threat from physical observables may be underestimated. Another urgent problem is how to secure a cryptographic system in the exposure of unavoidable leakage. Although many countermeasures have been developed, their effectiveness pends empirical verification and the side channel security needs to be evaluated systematically. The research in this dissertation focuses on two topics, leakage-model independent side channel analysis and security evaluation, which are described from three perspectives: leakage detection, exploitation and quantification. To free side channel analysis from the complicated procedure of leakage modeling, an observation to observation comparison approach is proposed. Several attacks presented in this work follow this approach. They exhibit efficient leakage detection and exploitation under various leakage models and implementations. More importantly, this achievement no longer relies on or even requires precise leakage modeling. For the security evaluation, a weak maximum likelihood approach is proposed. It provides a quantification of the loss of full key security due to the presence of side channel leakage. A constructive algorithm is developed following this approach. The algorithm can be used by security lab to measure the leakage resilience. It can also be used by a side channel adversary to determine whether limited side channel information suffices the full key recovery at affordable expense

The Side-Channel Metrics Cheat Sheet

Side-channel attacks exploit a physical observable originating from a cryptographic device in order to extract its secrets. Many practically relevant advances in the field of side-channel analysis relate to security evaluations of cryptographic functions and devices. Accordingly, many metrics have been adopted or defined to express and quantify side-channel security. These metrics can relate to one another, but also conflict in terms of effectiveness, assumptions and security goals. In this work, we review the most commonly used metrics in the field of side-channel analysis. We provide a self-contained presentation of each metric, along with a discussion of its limitations. We practically demonstrate the metrics on examples of relevant implementations of the Advanced Encryption Standard (AES), and make the software implementation of the presented metrics available to the community as open source. This work, being beyond a survey of the current status of metrics, will allow researchers and practitioners to produce a well-informed security evaluation through a better understanding of its supporting and summarizing metrics

Cryptanalysis of Some AES-based Cryptographic Primitives

Current information security systems rely heavily on symmetric key cryptographic primitives as one of their basic building blocks. In order to boost the efficiency of the security systems, designers of the underlying primitives often tend to avoid the use of provably secure designs. In fact, they adopt ad hoc designs with claimed security assumptions in the hope that they resist known cryptanalytic attacks. Accordingly, the security evaluation of such primitives continually remains an open field. In this thesis, we analyze the security of two cryptographic hash functions and one block cipher. We primarily focus on the recent AES-based designs used in the new Russian Federation cryptographic hashing and encryption suite GOST because the majority of our work was carried out during the open research competition run by the Russian standardization body TC26 for the analysis of their new cryptographic hash function Streebog. Although, there exist security proofs for the resistance of AES- based primitives against standard differential and linear attacks, other cryptanalytic techniques such as integral, rebound, and meet-in-the-middle attacks have proven to be effective. The results presented in this thesis can be summarized as follows: Initially, we analyze various security aspects of the Russian cryptographic hash function GOST R 34.11-2012, also known as Streebog or Stribog. In particular, our work investigates five security aspects of Streebog. Firstly, we present a collision analysis of the compression function and its in- ternal cipher in the form of a series of modified rebound attacks. Secondly, we propose an integral distinguisher for the 7- and 8-round compression function. Thirdly, we investigate the one wayness of Streebog with respect to two approaches of the meet-in-the-middle attack, where we present a preimage analysis of the compression function and combine the results with a multicollision attack to generate a preimage of the hash function output. Fourthly, we investigate Streebog in the context of malicious hashing and by utilizing a carefully tailored differential path, we present a backdoored version of the hash function where collisions can be generated with practical complexity. Lastly, we propose a fault analysis attack which retrieves the inputs of the compression function and utilize it to recover the secret key when Streebog is used in the keyed simple prefix and secret-IV MACs, HMAC, or NMAC. All the presented results are on reduced round variants of the function except for our analysis of the malicious version of Streebog and our fault analysis attack where both attacks cover the full round hash function. Next, we examine the preimage resistance of the AES-based Maelstrom-0 hash function which is designed to be a lightweight alternative to the ISO standardized hash function Whirlpool. One of the distinguishing features of the Maelstrom-0 design is the proposal of a new chaining construction called 3CM which is based on the 3C/3C+ family. In our analysis, we employ a 4-stage approach that uses a modified technique to defeat the 3CM chaining construction and generates preimages of the 6-round reduced Maelstrom-0 hash function. Finally, we provide a key recovery attack on the new Russian encryption standard GOST R 34.12- 2015, also known as Kuznyechik. Although Kuznyechik adopts an AES-based design, it exhibits a faster diffusion rate as it employs an optimal diffusion transformation. In our analysis, we propose a meet-in-the-middle attack using the idea of efficient differential enumeration where we construct a three round distinguisher and consequently are able to recover 16-bytes of the master key of the reduced 5-round cipher. We also present partial sequence matching, by which we generate, store, and match parts of the compared parameters while maintaining negligible probability of matching error, thus the overall online time complexity of the attack is reduced