Horizontal Clustering Side-Channel Attacks on Embedded ECC Implementations (Extended Version)

Side-channel attacks are a threat to cryptographic algorithms running on embedded devices. Public-key cryptosystems, including elliptic curve cryptography (ECC), are particularly vulnerable because their private keys are usually long-term. Well known countermeasures like regularity, projective coordinates and scalar randomization, among others, are used to harden implementations against common side-channel attacks like DPA. Horizontal clustering attacks can theoretically overcome these countermeasures by attacking individual side-channel traces. In practice horizontal attacks have been applied to overcome protected ECC implementations on FPGAs. However, it has not been known yet whether such attacks can be applied to protected implementations working on embedded devices, especially in a non-profiled setting. In this paper we mount non-profiled horizontal clustering attacks on two protected implementations of the Montgomery Ladder on Curve25519 available in the µNaCl library targeting electromagnetic (EM) emanations. The first implementation performs the conditional swap (cswap) operation through arithmetic of field elements (cswap-arith), while the second does so by swapping the pointers (cswap-pointer). They run on a 32-bit ARM Cortex-M4F core. Our best attack has success rates of 97.64% and 99.60% for cswap-arith and cswap-pointer, respectively. This means that at most 6 and 2 bits are incorrectly recovered, and therefore, a subsequent brute-force can fix them in reasonable time. Furthermore, our horizontal clustering framework used for the aforementioned attacks can be applied against other protected implementations

Evolutionary-enhanced quantum supervised learning model

Quantum supervised learning, utilizing variational circuits, stands out as a promising technology for NISQ devices due to its efficiency in hardware resource utilization during the creation of quantum feature maps and the implementation of hardware-efficient ansatz with trainable parameters. Despite these advantages, the training of quantum models encounters challenges, notably the barren plateau phenomenon, leading to stagnation in learning during optimization iterations. This study proposes an innovative approach: an evolutionary-enhanced ansatz-free supervised learning model. In contrast to parametrized circuits, our model employs circuits with variable topology that evolves through an elitist method, mitigating the barren plateau issue. Additionally, we introduce a novel concept, the superposition of multi-hot encodings, facilitating the treatment of multi-classification problems. Our framework successfully avoids barren plateaus, resulting in enhanced model accuracy. Comparative analysis with variational quantum classifiers from the technology's state-of-the-art reveal a substantial improvement in training efficiency and precision. Furthermore, we conduct tests on a challenging dataset class, traditionally problematic for conventional kernel machines, demonstrating a potential alternative path for achieving quantum advantage in supervised learning for NISQ era

Study of the Wind Speed Forecasting Applying Computational Intelligence

The conventional sources of energy such as oil, natural gas, coal, or nuclear are finite and generate environmental pollution. Alternatively, renewable energy source like wind is clean and abundantly available in nature. Wind power has a huge potential of becoming a major source of renewable energy for this modern world. It is a clean, emission-free power generation technology. Wind energy has been experiencing very rapid growth in Brazil and in Uruguay; therefore, it’s a promising industry in these countries. Thus, this rapid expansion can bring several regional benefits and contribute to sustainable development, especially in places with low economic development. Therefore, the scope of this chapter is to estimate short-term wind speed forecasting applying computational intelligence, by recurrent neural networks (RNN), using anemometers data collected by an anemometric tower at a height of 100.0 m in Brazil (tropical region) and 101.8 m in Uruguay (subtropical region), both Latin American countries. The results of this study are compared with wind speed prediction results from the literature. In one of the cases investigated, this study proved to be more appropriate when analyzing evaluation metrics (error and regression) of the prediction results obtained by the proposed model

Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms

The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data

Projeto de viabilidade de implantação de píer turístico em Pontal do Paraná - Estudo de caso : desembocadura canal do Departamento Nacional de Obras e Saneamento - DNOS

Orientador: Gustavo Pacheco TomasMonografia (graduação) - Universidade Federal do Paraná, Campus Pontal do Paraná, Centro de Estudos do Mar, Curso de Graduação em Engenharia Civil.Inclui referênciasResumo : O trabalho a seguir apresenta um estudo de viabilidade de construção de um píer turístico na desembocadura do canal do Departamento Nacional de Obras de Saneamento (DNOS), localizado no balneário de Pontal do Sul, em Pontal do Paraná/PR. O estudo foi baseado em propostas de outros píeres ao redor do mundo e aplicado considerando as características da região e dos turistas que costumam frequentá-la. O layout adotado foi feito através de softwares Building Information Modeling (BIM) e a partir do modelo foram feitas estimativas de cargas e da capacidade de resistência para contemplar um quantitativo aproximado de pilares e demais estruturas para composição de custos. Além dos custos, também foram estimadas receitas para permitir a execução do estudo de viabilidade. Dentre os resultados obtidos destacam-se a aplicação do modelo Triple Bottom Line (TBL), para que a construção do píer siga uma proposta sustentável, e o estudo de viabilidade através do uso de indicadores como Valor Presente Líquido (VPL), para análise de rentabilidade, e o Período de Recuperação do Investimento (Payback Period), para análise de risco

Exposure and dose assessment of school children to air pollutants in a tropical coastal-urban area

This study estimates exposure and inhaled dose to air pollutants of children residing in a tropical coastal-urban area in Southeast Brazil. For that, twenty-one children filled their time-activities diaries and wore the passive samplers to monitor NO2. The personal exposure was also estimated using data provided by the combination of WRF-Urban/GEOS-Chem/CMAQ models, and the nearby monitoring station. Indoor/outdoor ratios were used to consider the amount of time spent indoors by children in homes and schools. The model's performance was assessed by comparing the modelled data with concentrations measured by urban monitoring stations. A sensitivity analyses was also performed to evaluate the impact of the model's height on the air pollutant concentrations. The results showed that the mean children's personal exposure to NO2 predicted by the model (22.3 μg/m3) was nearly twice to those measured by the passive samplers (12.3 μg/m3). In contrast, the nearest urban monitoring station did not represent the personal exposure to NO2 (9.3 μg/m3), suggesting a bias in the quantification of previous epidemiological studies. The building effect parameterisation (BEP) together with the lowering of the model height enhanced the air pollutant concentrations and the exposure of children to air pollutants. With the use of the CMAQ model, exposure to O3, PM10, PM2.5, and PM1 was also estimated and revealed that the daily children's personal exposure was 13.4, 38.9, 32.9, and 9.6 μg/m3, respectively. Meanwhile, the potential inhalation daily dose was 570-667 μg for PM2.5, 684-789 μg for PM10, and 163-194 μg for PM1, showing to be favourable to cause adverse health effects. The exposure of children to air pollutants estimated by the numerical model in this work was comparable to other studies found in the literature, showing one of the advantages of using the modelling approach since some air pollutants are poorly spatially represented and/or are not routinely monitored by environmental agencies in many regions