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

Software piracy and reverse engineering cause significant damage to intellectual property: the commercial value of unlicensed software installed worldwide has been estimated at over USD 46 billion. Traditional protection methods rely on static encryption keys or on obfuscation, and they are not resistant to modern analysis techniques such as differential computation analysis (DCA). This study addresses the single point of failure in software protection systems by developing a new key management architecture. The paper introduces a distributed split-key software-hardware framework. A deterministic decryption key is divided into three parts: a local key derived from the unique properties of the machine's hardware (hardware fingerprint), a server key issued after authenticated online activation, and a key sealed in a Trusted Platform Module (TPM) and bound to the platform's Platform Configuration Registers (PCRs). The final key is assembled exclusively in RAM during program execution through an XOR operation and is erased immediately after use. A C#/.NET 8 reference implementation was evaluated on a single Windows platform with a discrete TPM 2.0 module. Changing any of the five hardware identifiers or the PCR state prevented decryption, the assembled key remained in memory for a median of 15.6 µs, key assembly took 0.3 µs, and the one-time TPM sealing took about 67 ms. The runtime overhead stayed below 5% for typical protected functions. The analysis also identifies the remaining limitations of the design: code harvesting through legitimate decryption, binary patching that does not affect PCR 0 and PCR 7, clock rollback during the offline grace period, and loss of access after legitimate firmware or Secure Boot updates. The framework binds execution to authorized hardware and raises the cost of reverse engineering.

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.