Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Ukraine's top military commander, Oleksandr Syrskii, said the army's situation has "significantly worsened" in parts of the southeastern Zaporizhia region amid fierce fighting with Russian forces. Ukrainian President Volodymyr Zelenskyy said in a post on X that he had received an update from Syrskii, which conveyed that the situation" remains difficult" in the Zaporizhia region, as well as in the direction of the embattled city of Pokrovsk .

How much of Europe's oil still comes from Russia? The European Union is preparing to adopt a new round of sweeping sanctions against Russian energy exports on Thursday, a day after United States President Donald Trump imposed similar measures against Moscow amid setbacks to his efforts at diplomacy with Vladimir Putin. These steps come as Russia and Ukraine are increasingly targeting each other's energy infrastructure in an attempt to make it economically harder to wage war. On the ground, Russia's war in Ukraine remained stagnant. Russia claimed it had taken another handful of villages during the past week - Tykhe and Pishchane in Kharkiv, Novopavlivka, Chunyshyne and Pleshcheyevka in Donetsk, Poltavka in Zaporizhia and Privillia in Dnipropetrovsk. On the whole, however, Ukrainian front lines remained resilient and Russia scored no major breakthrough.

-- Due to the increasing number of tasks that are solved on remote servers, identifying and classifyi ng traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. However, such ML models are also subject to attacks that affect the classification result of network traffic. To protect models, we proposed a solution based on an autoencoder. The threat landscape for industrial systems is rapidly evolving, with attack s becoming targeted and motivated.

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of detecting distorted data. The dynamics of changes in the quality indicators of the regression model were analyzed in conditions without adversarial attacks, during an adversarial attack and when the distorted data was isolated. It is shown that an adversarial FGSM attack with gradient maximization leads to an increase in the value of the MSE metric by 33% and a decrease in the R2 indicator by 10% on average. The LightGBM binary classifier effectively identifies data with adversarial anomalies with 98% accuracy. Regression machine learning models are susceptible to adversarial attacks, but rapid analysis of network traffic and data transmitted over the network makes it possible to identify malicious activity

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems, synthesis of adversarial examples, execution of adversarial attacks on machine learning models, fine tuning of large language models for detecting adversarial attacks, explainability of decisions on detecting cybersecurity incidents based on the prompts technique. Scientific novelty: A binary classification of data poisoning attacks was performed using large language models, and the possibility of using large language models for investigating cybersecurity incidents in the latest generation wireless networks was investigated. The result of research: Fine-tuning of large language models was performed on the prepared data of the emulated wireless network segment. Six large language models were compared for detecting adversarial attacks, and the capabilities of explaining decisions made by a large language model were investigated. The Gemma-7b model showed the best results according to the metrics Precision = 0.89, Recall = 0.89 and F1-Score = 0.89. Based on various explainability prompts, the Gemma-7b model notes inconsistencies in the compromised data under study, performs feature importance analysis and provides various recommendations for mitigating the consequences of adversarial attacks. Large language models integrated with binary classifiers of network threats have significant potential for practical application in the field of cybersecurity incident investigation, decision support and assessing the effectiveness of measures to counter information security threats.

The Ukrainian Air Force said Russia launched four missiles and 131 drones towards Ukraine overnight. The Air Force also said that 72 of the drones were destroyed while 59 disappeared without reaching their targets. Moscow's Ministry of Defence said its troops intercepted and destroyed 55 Ukrainian drones in six Russian regions overnight. Six drones were downed in Voronezh where, according to the region's Governor Aleksandr Gusev, falling debris started a blaze just six days after remnants of another intercepted drone triggered an earlier fire. Kyiv's military claimed responsibility for attacking an aviation manufacturing plant in Russia's Smolensk region where "combat aircraft[s] are being modernised and manufactured", as well as an attack on Voronezh which resulted in a fuel depot fire.

The field of machine learning is developing rapidly and is being used in various fields of science and technology. In this way, machine learning can be used to optimize the functions of latest generation data networks such as 5G and 6G. This also applies to functions at a lower level. A feature of the use of machine learning in the radio path for targeted radiation generation in modern ultra-massive MIMO, reconfigurable intelligent interfaces and other technologies is the complex acquisition and processing of data from the physical layer. Additionally, adversarial measures that manipulate the behaviour of intelligent machine learning models are becoming a major concern, as many machine learning models are sensitive to incorrect input data. To obtain data on attacks directly from processing service information, a simulation model is proposed that works in conjunction with machine learning applications.

Modern machine learning techniques in the natural language processing domain can be used to automatically generate scripts for goal-oriented dialogue systems. The current article presents a general framework for studying the automatic generation of scripts for goal-oriented dialogue systems. A method for preprocessing dialog data sets in JSON format is described. A comparison is made of two methods for extracting user intent based on BERTopic and latent Dirichlet allocation. A comparison has been made of two implemented algorithms for classifying statements of users of a goal-oriented dialogue system based on logistic regression and BERT transformer models. The BERT transformer approach using the bert-base-uncased model showed better results for the three metrics Precision (0.80), F1-score (0.78) and Matthews correlation coefficient (0.74) in comparison with other methods.

The steadily high demand for cash contributes to the expansion of the network of Bank payment terminals. To optimize the amount of cash in payment terminals, it is necessary to minimize the cost of servicing them and ensure that there are no excess funds in the network. The purpose of this work is to create a cash management system in the network of payment terminals. The article discusses the solution to the problem of determining the optimal amount of funds to be loaded into the terminals, and the effective frequency of collection, which allows to get additional income by investing the released funds. The paper presents the results of predicting daily cash withdrawals at ATMs using a triple exponential smoothing model, a recurrent neural network with long short-term memory, and a model of singular spectrum analysis. These forecasting models allowed us to obtain a sufficient level of correct forecasts with good accuracy and completeness. The results of forecasting cash withdrawals were used to build a discrete optimal control model, which was used to develop an optimal schedule for adding funds to the payment terminal. It is proved that the efficiency and reliability of the proposed model is higher than that of the classical Baumol-Tobin inventory management model: when tested on the time series of three ATMs, the discrete optimal control model did not allow exhaustion of funds and allowed to earn on average 30% more than the classical model.

When humans leave Earth for good, they're going to need somewhere to stay. Something like a big bouncy castle for kids, but built to house astronauts and solar system colonists and tourists looking for an out-of-this-world vacation. It sounds like a sci-fi fever dream, but it's becoming reality. On Friday, SpaceX will launch a so-called "expandable"--a prototype called the Bigelow Expandable Activity Module--to the International Space Station. It will remain there, attached to the Tranquility module, for two years.