According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations.

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Secure Chip-Off Method with Acoustic-based Fault Diagnostics for IoT and Smart Grid Data Recovery

While processing polymetallic ores at the non-ferrous metallurgy problems arises connecting with the excellence of production and the efficient applying the technological devices-firing furnace and crusher machine. In earlier time, similar questions were solved due to the big practice experiences and using a mathematical modeling method. The mathematical model for optimizing those operating mode is a very complex and hard to calculation. Performing computations is needed too much time and many resources. Because the control of the agglomeration furnaces and other machines are including complex multiparameter processes. The method of the math modeling for optimization the operating mode to the firing furnace is replaced with modeling based on the neural network that is here a new method. The results obtained have shown that proposed methods based on the neural network modeling of metallurgical processes allow determining more accurate and adequate results of calculations than mathematical modeling by the traditional program. The use of new approaches for modeling the technological processes at the non-ferrous metallurgy gives opportunity to enhance an effectiveness of production excellence and to enhance an efficient applying the heat-energy equipment while a firing the sulfide polymetallic ores of non-ferrous metallurgy

В статье рассматривается работа по разработке автоматизированной системы сбора и подготовки данных полученных из ПО системы мониторинга ЛВС PRTG Network Monitor. Цель статьи – выявить систематический подход к выявлению проблем, смоделировать бизнес-процессы согласно поставленным задачам и определить функции внедряемых решений. Авторы подчеркивают необходимость наличия систем мониторинга и хранения данных для оптимизации работ служб поддержки ЛВС, а также для дальнейшего внедрения других систем, например, системы сбора и подготовки данных для дальнейшего применения в области машинного прогнозирования. По вышесказанным причинам авторы рассмотрели возможность написания скрипта, который удовлетворял бы поставленным задачам. А именно: экономическая дешевизна при разработке, а не покупке готового софта, возможность работы скрипта с исходящими данными ПО PRTG Network Monitor, пригодный к чтению и анализу исходящих данных системой машинного прогнозирования, объединение максимально возможного числа таблиц в единую таблицу данных, и пр.

This study presents «trajectory_analyzer», a Python-based system designed for the forensic analysis and visualization of geolocation data extracted from mobile devices. With the increasing volume of spatial-temporal data collected from sources such as GPS, Wi-Fi, and image metadata, forensic professionals face growing challenges in structuring and interpreting mobility patterns. Existing solutions often lack flexibility, require supervised models, or depend on proprietary infrastructure. Our approach applies an unsupervised DBSCAN-based trajectory clustering method, temporal ordering, and a real-time web map interface to reveal behavioral insights without the need for manual labeling or cloud services. Compared to prior research, the system improves spatial accuracy, source transparency, and visual clarity. Experimental results show that the proposed clustering method identifies movement clusters and transitions with high precision and responsiveness, while maintaining full offline operability. However, this improvement comes at the expense of more local storage because of embedded map tiles. Overall, this work provides a practical, understandable, and independent foundation for investigators dealing with unstructured multi-source geolocation data.