Cryptanalysis of a family of self-synchronizing chaotic stream ciphers

Unimodal maps have been broadly used as a base of new encryption strategies. Recently, a stream cipher has been proposed in the literature, whose keystream is basically a symbolic sequence of the (one-parameter) logistic map or of the tent map. In the present work a thorough analysis of the keystream is made which reveals the existence of some serious security problemsComment: 10 pages, 6 figure

Estimation of the control parameter from symbolic sequences: Unimodal maps with variable critical point

The work described in this paper can be interpreted as an application of the order patterns of symbolic dynamics when dealing with unimodal maps. Specifically, it is shown how Gray codes can be used to estimate the probability distribution functions (PDFs) of the order patterns of parametric unimodal maps. Furthermore, these PDFs depend on the value of the parameter, what eventually provides a handle to estimate the parameter value from symbolic sequences (in form of Gray codes), even when the critical point depends on the parameter.Comment: 10 pages, 14 figure

A chaotic switched-capacitor circuit for characteristic CMOS noise distributions generation

A switched-capacitor circuit is proposed for the generation of noise resembling the typical noise spectral density of MOS devices. The circuit is based on the combination of two chaotic maps, one generating 1/f noise (hopping map) and the other generating white noise (Bernoulli map). Through a programmable weighted adder stage, the contribution of each map can be controlled and, thereby, the position of the corner frequency. Behavioral models simulations were carried out to prove the correct functionality of the proposed approach.Ministerio de Economía y Competitividad TEC2016-80923-

A new simple technique for improving the random properties of chaos-based cryptosystems

A new technique for improving the security of chaos-based stream ciphers has been proposed and tested experimentally. This technique manages to improve the randomness properties of the generated keystream by preventing the system to fall into short period cycles due to digitation. In order to test this technique, a stream cipher based on a Skew Tent Map algorithm has been implemented on a Virtex 7 FPGA. The randomness of the keystream generated by this system has been compared to the randomness of the keystream generated by the same system with the proposed randomness-enhancement technique. By subjecting both keystreams to the National Institute of Standards and Technology (NIST) tests, we have proved that our method can considerably improve the randomness of the generated keystreams. In order to incorporate our randomness-enhancement technique, only 41 extra slices have been needed, proving that, apart from effective, this method is also efficient in terms of area and hardware resources

A 1 Gbps Chaos-Based Stream Cipher Implemented in 0.18 m CMOS Technology

In this work, a novel chaos-based stream cipher based on a skew tent map is proposed and implemented in a 0.18 µm CMOS (Complementary Metal-Oxide-Semiconductor) technology. The proposed ciphering algorithm uses a linear feedback shift register that perturbs the orbits generated by the skew tent map after each iteration. This way, the randomness of the generated sequences is considerably improved. The implemented stream cipher was capable of achieving encryption speeds of 1 Gbps by using an approximate area of ~20,000 2-NAND equivalent gates, with a power consumption of 24.1 mW. To test the security of the proposed cipher, the generated keystreams were subjected to National Institute of Standards and Technology (NIST) randomness tests, proving that they were undistinguishable from truly random sequences. Finally, other security aspects such as the key sensitivity, key space size, and security against reconstruction attacks were studied, proving that the stream cipher is secure

Rethinking continual learning approach and study out-of-distribution generalization algorithms

L'un des défis des systèmes d'apprentissage automatique actuels est que les paradigmes d'IA standard ne sont pas doués pour transférer (ou exploiter) les connaissances entre les tâches. Alors que de nombreux systèmes ont été formés et ont obtenu des performances élevées sur une distribution spécifique d'une tâche, il est pas facile de former des systèmes d'IA qui peuvent bien fonctionner sur un ensemble diversifié de tâches qui appartiennent aux différentes distributions. Ce problème a été abordé sous différents angles dans différents domaines, y compris l'apprentissage continu et la généralisation hors distribution. Si un système d'IA est formé sur un ensemble de tâches appartenant à différentes distributions, il pourrait oublier les connaissances acquises lors des tâches précédentes. En apprentissage continu, ce processus entraîne un oubli catastrophique qui est l'un des problèmes fondamentaux de ce domaine. La première projet de recherche dans cette thèse porte sur la comparaison d'un apprenant chaotique et d'un naïf configuration de l'apprentissage continu. La formation d'un modèle de réseau neuronal profond nécessite généralement plusieurs itérations, ou époques, sur l'ensemble de données d'apprentissage, pour mieux estimer les paramètres du modèle. La plupart des approches proposées pour ce problème tentent de compenser les effets de mises à jour des paramètres dans la configuration incrémentielle par lots dans laquelle le modèle de formation visite un grand nombre de échantillons pour plusieurs époques. Cependant, il n'est pas réaliste de s'attendre à ce que les données de formation soient toujours alimenté au modèle. Dans ce chapitre, nous proposons un apprenant de flux chaotique qui imite le chaotique comportement des neurones biologiques et ne met pas à jour les paramètres du réseau. De plus, il peut fonctionner avec moins d'échantillons par rapport aux modèles d'apprentissage en profondeur sur les configurations d'apprentissage par flux. Fait intéressant, nos expériences sur différents ensembles de données montrent que l'apprenant de flux chaotique a moins d'oubli catastrophique de par sa nature par rapport à un modèle CNN en continu apprentissage. Les modèles d'apprentissage en profondeur ont une performance de généralisation hors distribution naïve où la distribution des tests est inconnue et différente de la formation. Au cours des dernières années, il y a eu eu de nombreux projets de recherche pour comparer les algorithmes hors distribution, y compris la moyenne et méthodes basées sur les scores. Cependant, la plupart des méthodes proposées ne tiennent pas compte du niveau de difficulté de tâches. Le deuxième projet de recherche de cette thèse, l'analyse de certains éléments logiques et pratiques les forces et les inconvénients des méthodes existantes de comparaison et de classement hors distribution algorithmes. Nous proposons une nouvelle approche de classement pour définir les ratios de difficulté des tâches afin de comparer les algorithmes de généralisation hors distribution. Nous avons comparé la moyenne, basée sur le score, et des classements basés sur la difficulté de quatre tâches sélectionnées du benchmark WILDS et cinq algorithmes hors distribution populaires pour l'expérience. L'analyse montre d'importantes changements dans les ordres de classement par rapport aux approches de classement actuelles.One of the challenges of current machine learning systems is that standard AI paradigms are not good at transferring (or leveraging) knowledge across tasks. While many systems have been trained and achieved high performance on a specific distribution of a task, it is not easy to train AI systems that can perform well on a diverse set of tasks that belong to different distributions. This problem has been addressed from different perspectives in different domains including continual learning and out-of-distribution generalization. If an AI system is trained on a set of tasks belonging to different distributions, it could forget the knowledge it acquired from previous tasks. In continual learning, this process results in catastrophic forgetting which is one of the core issues of this domain. The first research project in this thesis focuses on the comparison of a chaotic learner and a naive continual learning setup. Training a deep neural network model usually requires multiple iterations, or epochs, over the training data set, to better estimate the parameters of the model. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to the model. In this chapter, we propose a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not update network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setups. Interestingly, our experiments on different datasets show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning. Deep Learning models have a naive out-of-distribution~(OoD) generalization performance where the testing distribution is unknown and different from the training. In the last years, there have been many research projects to compare OoD algorithms, including average and score-based methods. However, most proposed methods do not consider the level of difficulty of tasks. The second research project in this thesis, analysis some logical and practical strengths and drawbacks of existing methods for comparing and ranking OoD algorithms. We propose a novel ranking approach to define the task difficulty ratios to compare OoD generalization algorithms. We compared the average, score-based, and difficulty-based rankings of four selected tasks from the WILDS benchmark and five popular OoD algorithms for the experiment. The analysis shows significant changes in the ranking orders compared with current ranking approaches

Detecting the temporal structure of intermittent phase locking

This study explores a method to characterize temporal structure of intermittent phase locking in oscillatory systems. When an oscillatory system is in a weakly synchronized regime away from a synchronization threshold, it spends most of the time in parts of its phase space away from synchronization state. Therefore characteristics of dynamics near this state (such as its stability properties/Lyapunov exponents or distributions of the durations of synchronized episodes) do not describe system's dynamics for most of the time. We consider an approach to characterize the system dynamics in this case, by exploring the relationship between the phases on each cycle of oscillations. If some overall level of phase locking is present, one can quantify when and for how long phase locking is lost, and how the system returns back to the phase-locked state. We consider several examples to illustrate this approach: coupled skewed tent maps, which stability can be evaluated analytically, coupled R\"{o}ssler and Lorenz oscillators, undergoing through different intermittencies on the way to phase synchronization, and a more complex example of coupled neurons. We show that the obtained measures can describe the differences in the dynamics and temporal structure of synchronization/desynchronization events for the systems with similar overall level of phase locking and similar stability of synchronized state.Comment: 12 pages, 10 figures. The paper will appear in Phys. Rev.