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An important aspect of the design of effective machine learning algorithms is the complexity analysis of classification problems. In this paper, we propose a study aimed at determining the relation between the number of adjacent inputs with different labels and the required number of examples for the task of inducing a classification model. To this aim, we first quantified the adjacent inputs with different labels as a property, using a measure denoted as Neighbour Input Variation (NIV). We analyzed the relation that NIV has to random data and overfitting. We then demonstrated that a threshold of NIV may determine if a classification model can generalize to unseen data. We also presented a case study aimed at analyzing threshold neural networks and the required first hidden layer size in function of NIV. Finally, we performed experiments with five popular algorithms analyzing the relation between NIV and the classification error on problems with few dimensions. We conclude that functions whose similar inputs have different outputs with high probability, considerably reduce the generalization capacity of classification algorithms.

Since the discovery of the physical random functions and their subsequent refinement into physical unclonable functions (PUF), a great effort has been made in developing and characterizing these objects attending to their physical properties as well as conceiving a myriad of different examples in the search for a better application-specificity and suitability. However, comparatively little time has been devoted to the analysis of entropy extraction algorithms beyond the recognition of some limitations due to the environment influencing the PUF behavior. In this article we focus on well known PUF candidates based on ring oscillator delay, which are ideal for FPGA prototyping due to their tolerance to asymmetries in routing. We have studied the impact that different digitization algorithms of the responses have over their security properties. Specifically, we have analyzed the response probability distributions that arise from some popular techniques of digitization called “compensated measuring” methods, highlighting their lack of uniformity and how this might translate into cryptanalytically exploitable vulnerabilities. Furthermore, we propose a new family of digitization schemes named k-modular that exhibit both uniformity in response distribution and high entropy density on both physical and response space.

In some encryption systems it is necessary to preserve the format and length of the encrypted data. This kind of encryption is called FPE (Format Preserving Encryption). Currently, only two AES (Advanced Encryption Standard) modes of operation recommended by the NIST (National Institute of Standards and Technology) are able to implement FPE algorithms, FF1 and FF3. These modes work in an electronic codebook fashion and can be configured to encrypt databases with an arbitrary format and length. However, there are no stream cipher proposals able to implement FPE encryption for high data rate information flows. The main novelty of this work is a new block cipher operation mode proposal to implement an FPE algorithm in a stream cipher fashion. It has been called CTR-MOD and it is based on a standard block cipher working in CTR (Counter) mode and a modulo operation. The confidentiality of this mode is analyzed in terms of its IND- CPA (Indistinguishability under Chosen Plaintext Attack) advantage of any adversary attacking it. Moreover, the encryption scheme has been implemented on an FPGA (Field Programmable Gate Array) and has been integrated in a Gigabit Ethernet interface to test an encrypted optical link with a real high data rate traffic flow.

In this work a new self-synchronized symmetric encryption solution for high speed communication systems necessary to preserve the format of the plaintext is proposed, developed and tested. This new encryption mechanism is based on the block cipher operation mode called PSCFB (Pipelined Statistical Cipher Feedback) and the modulo operation. The confidentiality of this mode is analyzed in terms of its IND-CPA (Indistinguishability under Chosen-Plaintext Attack) advantage, concluding that it can be considered secure in the same way as traditional modes are. The encryption system has been integrated in the physical layer of a 1000Base-X Gigabit Ethernet Interface, where the 8b/10b symbol flow is encrypted at line rate. Moreover, an implementation of the proposed system has been carried out in an FPGA (Field Programmable Gate Array) device. Finally, an encrypted optical link has been tested with real Ethernet frames, getting maximum throughput and protecting the data traffic from passive eavesdroppers.

In this article, the possibility of using Galois ring oscillators to construct physically unclonable functions (PUFs) has been studied. The idea is to use novel PUF architectures, similar as the ring oscillator PUFs that, instead of comparing frequencies, compare the statistical bias of pairs of oscillators implemented in different locations. To study the viability of these systems, three different Galois oscillators have been implemented in several locations in several FPGAs and we have studied the main properties of their bias: repeatability, variability with the location, variability with the FPGA and spatial autocorrelation. Based on this study, we have determined that the bias of these oscillators meet the requirements that are needed to be used to construct a PUF. Finally, a PUF based on comparing the bias of neighboring 7-LUT Galois ring oscillators have been implemented and analyzed. The experimental results show that this PUF generates uniform responses that are highly reproducible and unique, making this PUF suitable for being used in identification applications.

Students’ participation and motivation are of great importance in their learning process. The decrease in attendance and difficulties in stimulating students’ activity makes it necessary to find new methodologies that can solve these problems. The use of game mechanics in non-ludic environments (Gamification) has begun to be of great interest in research, since it could increase the motivation and therefore the activity of the students. This study tries to verify if there is an existing relationship between gamification and a possible increase in student activity or between the cessation of gamification and student activity. In addition, it evaluates whether a greater student activity corresponds to a greater learning improvement. In the obtained results, no significant differences were found between the methodology or the activity with a learning improvement. According to the study, the simple use of gamified elements does not necessarily imply a solution to the problems posed. The students’ activity in a course of these characteristics does not imply a better learning improvement, the importance lies in the quality of the activity generated and not in the quantity.

The use of the mechanics and elements of games in non-ludic environments (Gamification) has increased in non-ludic contexts in various areas including education. This study focused on the perception that students have of the different elements of gamification in three different experiences, after which students were asked to answer a survey about the gamified elements of the course in which they participated. In the three experiences, Feedback, levels, points and missions stand out as the best valued, despite the fact that, for example, “time limit” were little valued by the students, it can be useful in the design of a gamified experience, but its use must be well regulated to avoid possible negative aspects and favor a good experience for the student. On the other hand, though badges were also been well valued by students in the longest experiences, in the shortest experience, it was one of the worst rated elements. Finally, the elements that students liked the least of all those analyzed, were the leaderboard and the blocked content.