Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method

This paper presents a computational approach to evaluate the resilience of electricity Distribution Networks (DNs) to cyber-physical failures. In our model, we consider an attacker who targets multiple DN components to maximize the loss of the DN operator. We consider two types of operator response: (i) Coordinated emergency response; (ii) Uncoordinated autonomous disconnects, which may lead to cascading failures. To evaluate resilience under response (i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is computationally challenging due to mixed-integer variables in the inner problem and non-convex constraints. Our solution approach is based on the Generalized Benders Decomposition method, which achieves a reasonable tradeoff between computational time and solution accuracy. Our approach involves modifying the Benders cut based on structural insights on power flow over radial DNs. We evaluate DN resilience under response (ii) by sequentially computing autonomous component disconnects due to operating bound violations resulting from the initial attack and the potential cascading failures. Our approach helps estimate the gain in resilience under response (i), relative to (ii)

Innovative Method of the Power Analysis

This paper describes an innovative method of the power analysis which presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. The proposed method analyzes power consumption of the AES (Advanced Encryption Standard) algorithm with neural network, which successively classifies the first byte of the secret key. This way of the power analysis is an entirely new approach and it is designed to combine the advantages of simple and differential power analysis. In the extreme case, this feature allows to determine the whole secret key of a cryptographic module only from one measured power trace. This attribute makes the proposed method very attractive for potential attackers. Besides theoretical design of the method, we also provide the first implementation results. We assume that the method will be certainly optimized to obtain more accurate classification results in the future

Towards joint decoding of binary Tardos fingerprinting codes

The class of joint decoder of probabilistic fingerprinting codes is of utmost importance in theoretical papers to establish the concept of fingerprint capacity. However, no implementation supporting a large user base is known to date. This article presents an iterative decoder which is, as far as we are aware of, the first practical attempt towards joint decoding. The discriminative feature of the scores benefits on one hand from the side-information of previously accused users, and on the other hand, from recently introduced universal linear decoders for compound channels. Neither the code construction nor the decoder make precise assumptions about the collusion (size or strategy). The extension to incorporate soft outputs from the watermarking layer is straightforward. An extensive experimental work benchmarks the very good performance and offers a clear comparison with previous state-of-the-art decoders.Comment: submitted to IEEE Trans. on Information Forensics and Security. - typos corrected, one new plot, references added about ECC based fingerprinting code

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

How low can you go? Using side-channel data to enhance brute-force key recovery

Side-channel analysis techniques can be used to construct key recovery attacks by observing a side-channel medium such as the power consumption or electromagnetic radiation of a device while is it performing cryptographic operations. These attack results can be used as auxiliary information in an enhanced brute-force key recovery attack, enabling the adversary to \emph{enumerate} the most likely keys first. We use algorithmic and implementation techniques to implement a time- and memory-efficient key \emph{enumeration} algorithm, and in tandem identify how to optimise throughput when bulk-verifying quantities of candidate AES-128 keys. We then explore how to best distribute the workload so that it can be deployed across a significant number of CPU cores and executed in parallel, giving an adversary the capability to enumerate a very large number of candidate keys. We introduce the tool \textsc{labynkyr}, developed in C++11, that can be deployed across any number of CPUs and workstations to enumerate keys in parallel. We conclude by demonstrating the effectiveness of our tool by successfully enumerating $2^{48}$ AES-128 keys in approximately 30 hours using a modest number of CPU cores, at an expected cost of only 700 USD using a popular cloud provider

Efficient Framework for Genetic-Algorithm-Based Correlation Power Analysis

Various Artificial Intelligence (AI) techniques are combined with classic side-channel methods to improve the efficiency of attacks. Among them, Genetic Algorithms based Correlation Power Analysis (GA-CPA) is proposed to launch attacks on hardware cryptosystems to extract the secret key efficiently. However, the convergence rate is unsatisfactory due to two problems: individuals of the initial population generally have low fitnesses, and the mutation operation is hard to generate high-quality components. In this paper, we give an analysis framework to solve them. Firstly, we employ lists of sorted candidate key bytes obtained with CPA to initialize the population with high quality candidates. Secondly, we guide the mutation operation with lists of candidate keys sorted according to fitnesses, which are obtained by exhausting the values of a certain key byte and calculating the corresponding correlation coefficients with the whole key. Thirdly, key enumeration algorithms are utilized to deal with ranked candidates obtained by the last generation of GA-CPA to improve the success rate further. Simulation experimental results show that our method reduces the number of traces by 33.3\% and 43.9\% compared to CPA with key enumeration and GA-CPA respectively when the success rate is fixed to 90\%. Real experiments performed on SAKURA-G confirm that the number of traces required in our method is much less than the numbers of traces required in CPA and GA-CPA. Besides, we adjust our method to deal with DPA contest v1 dataset, and achieve a better result of 40.76 traces than the winning proposal of 42.42 traces. The computation cost of our proposal is nearly 16.7\% of the winner

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