This article presents an innovative approach to video steganography called Stego-STFAN, as by using a cheap model process to use the temporal and spatial domains together, they end up presenting fine adjustments in each frame, the Stego-STFAN had a $PSN{R}_c$ metric of 27.03 and $PSN{R}_S$ of 23.09, which is close to the state-of-art. Steganography is the ability to hide a message so that third parties cannot perceive communication between them. Thus, one of the precautions in steganography is the size of the message you want to hide, as the security of the message is inversely proportional to its size. Inspired by this principle, video steganography appears to expand channels further and incorporate data into a message. To improve the construction of better stego-frames and recovered secrets, we propose a new architecture for video steganography derived from the Spatial-Temporal Adaptive Filter Network (STFAN) in conjunction with the Attention mechanism, which together generates filters and maps dynamic frames to increase the efficiency and effectiveness of frame processing, exploiting the redundancy present in the temporal dimension of the video, as well as fine details such as edges, fast-moving pixels and the context of secret and cover frames and by using the DWT method as another feature extraction level, having the same characteristics as when applied to an image file.

Passwords are a common user authentication method, and must be safeguarded by effective security measures. However, there are many cases of compromised user credentials in data breaches. This work studies RockYou2024, a massive data breach that occurred in July 2024 and exposed over 9 billion passwords. We investigate the passwords with regard to their lengths, entropy, use of personal information and common strings, and evaluation from zxcvbn, as well as making a comparative assessment of the results with previous password databases, namely RockYou2021 and RockYou, which was leaked in 2009. This analysis found that the passwords from RockYou2021 and RockYou2024 are significantly more secure than those from RockYou, which suggests an improvement in password creation awareness and policies. It was also noted that RockYou2021 and RockYou2024 have similar statistical distributions in all the analyses conducted. We have also found that the country of origin for most passwords within these databases is most likely to be the United States of America. These datasets were searched for passwords that are often used in industrial systems, which pose potential security risks in critical infrastructure sectors. Finally, we also propose passBiRVAE, a contextualized Bidirectional Recurrent Neural Network , used to generate passwords based on the RockYou2024 database. Future works should make further improvements to the results obtained from this model. However, there is a risk of threats to the validity of these analyses.

Volatility reflects the degree of variation in a time series, and a measurement of the stock performance in the energy sector can help one understand the pattern of fluctuations within this industry, as well as the factors that influence it. One of these factors could be the COVID-19 pandemic, which led to extreme volatility within the stock market in several economic sectors. It is essential to understand this regime of volatility so that robust financial strategies can be adopted to handle it. This study used stock data from the Yahoo! Finance API and data from the energy-price database from the US Energy Information Administration to conduct a comparative analysis of the volatility in the energy sector in Brazil and in the United States, as well as of the energy prices in California. The volatility in these time series were modeled using GARCH. The stock volatility regimes, both before and after COVID-19, were identified with a Markov switching model; the spillover index between the energy markets in the USA and in Brazil was evaluated with the Diebold–Yilmaz index; and the causality between the energy stock price and the energy prices was measured with the Granger causality test. The findings of this study show that (i) the volatility regime introduced by COVID-19 is still prevalent in Brazil and in the USA, (ii) the changes in the energy market in the US affect the Brazilian market significantly more than the reverse, and (iii) there is a causality relationship between the energy stock markets and the energy prices in California. These results may assist in the achievement of effective regulation and economic planning, while also supporting better market interventions. Also, acknowledging the persistent COVID-19-induced volatility can help with developing strategies for future crisis resilience.