The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more difficult. Consequently, many researchers approach this problem from a different angle, in which instead of selecting an existing activation function, an appropriate activation function is evolved for the problem at hand. In this paper, we demonstrate that evolutionary algorithms can evolve new activation functions for side-channel analysis (SCA), outperforming ReLU and other activation functions commonly applied to that problem. More specifically, we use Genetic Programming to define and explore candidate activation functions (neuroevolution) in the form of mathematical expressions that are gradually improved. Experiments with the ASCAD database show that this approach is highly effective compared to results obtained with standard activation functions and that it can match the state-of-the-art results from the literature. More precisely, the obtained results for the ASCAD fixed key dataset demonstrate that the evolved activation functions can improve the current state-of-the-art by achieving a guessing entropy of 287 for the Hamming weight model and 115 for the Identity leakage model, compared to 447 and 120 obtained in the literature.@en

Finding optimal adversarial dynamics between defenders and attackers in large network systems is a complex problem one can approach from several perspectives. The results obtained are often not satisfactory since they either concentrate on only one party or run very simplified scenarios that are hard to correlate with realistic settings. To truly find which are the most robust defensive strategies, the adaptive attacker ecosystem must be given as many degrees of freedom as possible, to model real attacking scenarios accurately. We propose a coevolutionary-based simulator called MOVE that can evolve both attack and defense strategies. To test it, we investigate several different but realistic scenarios, taking into account features such as network topology and possible applications in the network. The results show that the evolved strategies far surpass randomly generated strategies. Finally, the evolved strategies can help us to reach some more general conclusions for both attacker and defender sides.

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The profiled side-channel analysis represents the most powerful category of side-channel attacks. In this context, the security evaluator (i.e., attacker) gains access to a profiling device to build a precise model which is used to attack another device in the attacking phase. Mostly, it is assumed that the attacker has significant capabilities in the profiling phase, whereas the attacking phase is very restricted. We step away from this assumption and consider an attacker restricted in the profiling phase, while the attacking phase is less limited. We propose the concept of semi-supervised learning for side-channel analysis, where the attacker uses a small number of labeled measurements from the profiling phase as well as the unlabeled measurements from the attacking phase to build a more reliable model. Our results show that the semi-supervised concept significantly helps the template attack (TA) and its pooled version (TA_p). More specifically, for low noise scenario, the results for machine learning techniques and TA are often improved when only a small number of measurements is available in the profiling phase, while there is no significant difference in scenarios where the supervised set is large enough for reliable classification. For high noise scenario, TA_p and multilayer perceptron results are improved for the majority of inspected dataset sizes, while for high noise scenario with added countermeasures, we show a small improvement for TA_p, Naive Bayes and multilayer perceptron approaches for most inspected dataset sizes. Current results go in favor of using semi-supervised learning, especially self-training approach, in side-channel attacks.

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In this paper, we ask a question whether evolutionary algorithms can evolve cryptographic algorithms when no precise design criteria are given. Our strategy utilizes Cartesian Genetic Programming in the bi-level optimization setting with multiple populations trying to evolve a cryptographic algorithm and break it. To challenge our design paradigm, we consider a number of scenarios with varying criteria on the system and its security. We are able to obtain interesting results in several scenarios where the attacker is not able to understand the text with more than a random chance. Interestingly, our system is able to develop various versions of one-time pads, which are the only systems that ensure perfect secrecy. Although our system is far from practical, we consider it interesting since it gives good results that are also human-readable.@en

Finding cryptographic primitives satisfying certain properties is a difficult problem. In this domain, besides the algebraic constructions, researchers often use heuristics. There exists a set of interesting problems related to the notion of differential uniformity for a function F: F2n→ F2m. When n=m, then the best obtainable differential uniformity equals 2, since it is necessarily positive and even, and since examples of differentially 2-uniform functions are known. Heuristics are able to reach such functions; there is then some intuition that heuristics can be used for other open problems related to differential uniformity. When n > m > n/2, differential uniformity is bounded by 2n-m+2 from below (when m=n-2, by 6). Unfortunately, we know such functions only for dimensions equal to n=4,5. In this paper, we explore several evolutionary algorithms and problem sizes in order to find functions having differential uniformity equal to 6. Our results show that several solution encodings are able to find such functions but only in dimensions (4, 2) and (5, 3). Since differentially 6-uniform functions were known for those sizes before, our results can be used as a source of new functions in those dimensions and as an indicator that for (6, 4) such functions either do not exist or that it is extremely difficult to find them.

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Disjunct Matrices (DM) are a particular kind of binary matrices which have been especially applied to solve the Non-Adaptive Group Testing (NAGT) problem, where the task is to detect any configuration of t defectives out of a population of N items. Traditionally, the methods used to construct DM leverage on error-correcting codes and other related algebraic techniques. Here, we investigate the use of Evolutionary Algorithms to design DM and two of their generalizations, namely Resolvable Matrices (RM) and Almost Disjunct Matrices (ADM). After discussing the basic encoding used to represent the candidate solutions of our optimization problems, we define three fitness functions, each measuring the deviation of a generic binary matrix from being respectively a DM, an RM or an ADM. Next, we employ Estimation of Distribution Algorithms (EDA), Genetic Algorithms (GA), and Genetic Programming (GP) to optimize these fitness functions. The results show that GP achieves the best performances among the three heuristics, converging to an optimal solution on a wider range of problem instances. Although these results do not match those obtained by other state-of-the-art methods in the literature, we argue that our heuristic approach can generate solutions that are not expressible by currently known algebraic techniques, and sketch some possible ideas to further improve its performance.@en

Boolean functions have a prominent role in many real-world applications, which makes them a very active research domain. Throughout the years, various heuristic techniques proved to be an attractive choice for the construction of Boolean functions with different properties. One of the most important properties is nonlinearity, and in particular maximally nonlinear Boolean functions are also called bent functions. In this paper, instead of considering Boolean functions, we experiment with quaternary functions. The corresponding problem is much more difficult and presents an interesting benchmark as well as realworld applications. The results we obtain show that evolutionary metaheuristics, especially genetic programming, succeed in finding quaternary functions with the desired properties. The obtained results in the quaternary domain can also be translated into the binary domain, in which case this approach compares favorably with the state-of-the-art in Boolean optimization. Our techniques are able to find quaternary bent functions for up to 8 inputs, which corresponds to obtaining Boolean bent functions of 16 inputs.

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