Data breaches result in data loss, including personal, health, and financial information that are crucial, sensitive, and private. The breach is a security incident in which personal and sensitive data are exposed to unauthorized individuals, with the potential to incur several privacy concerns. As an example, the French newspaper Le Figaro breached approximately 7.4 billion records that included full names, passwords, and e-mail and physical addresses. To reduce the likelihood and impact of such breaches, it is fundamental to strengthen the security efforts against this type of incident and, for that, it is first necessary to identify patterns of its occurrence, primarily related to the number of data records leaked, the affected geographical region, and its regulatory aspects. To advance the discussion in this regard, we study a dataset comprising 428 worldwide data breaches between 2018 and 2019, providing a visualization of the related statistics, such as the most affected countries, the predominant economic sector targeted in different countries, and the median number of records leaked per incident in different countries, regions, and sectors. We then discuss the data protection regulation in effect in each country comprised in the dataset, correlating key elements of the legislation with the statistical findings. As a result, we have identified an extensive disclosure of medical records in India and government data in Brazil in the time range. Based on the analysis and visualization, we find some interesting insights that researchers seldom focus on before, and it is apparent that the real dangers of data leaks are beyond the ordinary imagination. Finally, this paper contributes to the discussion regarding data protection laws and compliance regarding data breaches, supporting, for example, the decision process of data storage location in the cloud.

Any network connected to the Internet is subject to cyber attacks. Strong security measures, forensic tools, and investigators contribute together to detect and mitigate those attacks, reducing the damages and enabling reestablishing the network to its normal operation, thus increasing the cybersecurity of the networked environment. This paper addresses the use of a forensic approach with Deep Packet Inspection to detect anomalies in the network traffic. As cyber attacks may occur on any layer of the TCP/IP networking model, Deep Packet Inspection is an effective way to reveal suspicious content in the headers or the payloads in any packet processing layer, excepting of course situations where the payload is encrypted. Although being efficient, this technique still faces big challenges. The contributions of this paper rely on the association of Deep Packet Inspection with forensics analysis to evaluate different attacks towards a Honeynet operating in a network laboratory at the University of Brasilia. In this perspective, this work could identify and map the content and behavior of attacks such as the Mirai botnet and brute-force attacks targeting various different network services. Obtained results demonstrate the behavior of automated attacks (such as worms and bots) and non-automated attacks (brute-force conducted with different tools). The data collected and analyzed is then used to generate statistics of used usernames and passwords, IP and services distribution, among other elements. This paper also discusses the importance of network forensics and Chain of Custody procedures to conduct investigations and shows the effectiveness of the mentioned techniques in evaluating different attacks in networks.

Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033.

A data breach is the unauthorized disclosure of sensitive personal data, and it impacts millions of individuals annually in the United States, as reported by Privacy Rights Clearinghouse. These breaches jeopardize the physical safety of the individuals whose data are exposed and result in substantial economic losses for the affected companies. To diminish the frequency and severity of data breaches in the future, it is imperative to research their causes and explore preventive measures. In pursuit of this goal, this study considers a dataset of data breach incidents affecting companies listed on the New York Stock Exchange and NASDAQ. This dataset has been augmented with additional information regarding the targeted company. This paper employs statistical visualizations of the data to clarify these incidents and assess their consequences on the affected companies and individuals whose data were compromised. We then propose mitigation controls based on established frameworks such as the NIST Cybersecurity Framework. Additionally, this paper reviews the compliance scenario by examining the relevant laws and regulations applicable to each case, including SOX, HIPAA, GLBA, and PCI-DSS, and evaluates the impacts of data breaches on stock market prices. We also review guidelines for appropriately responding to data leaks in the U.S., for compliance achievement and cost reduction. By conducting this analysis, this work aims to contribute to a comprehensive understanding of data breaches and empower organizations to safeguard against them proactively, improving the technical quality of their basic services. To our knowledge, this is the first paper to address compliance with data protection regulations, security controls as countermeasures, financial impacts on stock prices, and incident response strategies. Although the discussion is focused on publicly traded companies in the United States, it may also apply to public and private companies worldwide.

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.

This manuscript explores the role of green hydrogen produced through ethanol reforming in accelerating Brazil’s transition to a low-carbon economic framework. Despite ongoing efforts to lessen carbon dependence, Brazil’s reliance on biofuels and other renewable energy sources remains inadequate for fully achieving its decarbonization objectives. Green hydrogen presents a vital opportunity to boost energy sustainability, especially in sectors that are challenging to decarbonize, such as industry and transportation. By analyzing Brazil’s input–output (I-O) table, using data from the Brazilian Institute of Geography and Statistics (IBGE), this study evaluates the macroeconomic potential of green hydrogen, focusing on GDP growth and employment generation. Furthermore, the research explores green hydrogen systems’ economic feasibility and potential impact on future energy policies, offering valuable insights for stakeholders and decision-makers. In addition, this investigation highlights Brazil’s abundant renewable resources and identifies the infrastructural investments necessary to support a green hydrogen economy. The findings aim to strengthen Brazil’s national decarbonization strategy and serve as a model for other developing nations transitioning to clean energy.