The information stage of human society development which began at the end of the last century results in the fact that the state of information security has become directly dependent not only on the information processing technical systems and features but also on the perception of information at the level of individual psychological qualities. The use of information aggression and special information operations including those performed in modern geopolitics at the international and domestic levels for population management, during electoral campaigns is gaining enormous scope. The tasks of early information impact detection, situation development modeling in the information space necessitate the development of specialized models reproducing information confrontation. The major contradiction in the development of such models is that the more relevant and adaptive these models are the more complex and resource-intensive they become. At the same time, oversimplifying the information confrontation process makes such models inconsistent with real risks. This article gives a brief overview of modern information confrontation models and concepts. It is described the basic principles of the construction of an information confrontation ontological model: such key elements as subjects, objects, actual impacts, and the basic characteristics of each element are identified. An attempt has been made to develop a universal information confrontation ontological model. It has been also proposed a multipart tuple of information confrontation representation. This article is the beginning of a separate research project on information confrontation modeling, which will be further developed in papers to follow.

Wireless Sensor Networks (WSNs) play a crucial role in applications such as remote monitoring, surveillance, and the Internet of Things (IoT). Addressing the challenge of energy consumption is paramount in WSN design due to the finite nature of energy resources. In cluster-based WSNs, cluster heads (CH) perform vital tasks like data collection, data aggregation, and exchange with the base station. Therefore, achieving efficient load balancing for CHs is crucial for maximizing network longevity. Previous studies have considered load balancing with optimal CH selection, but the issue of data redundancy is not addressed. Data redundancy in processing and transmitting information to analysis centres significantly depletes sensor resources like (energy, bandwidth and such). This paper proposes a novel energy-efficient data aggregation approach with data compression termed C-EWaOA that is (Compression based Enhanced Walrus Optimization with a cognitive factor). The non-negative matrix factorization (NMF) is used to compress the data and remove the redundant information. This way, the proposed data aggregation scheme reduces packet delivery Ratio, resulting in low data-rate communication. Simultaneously, data compression minimizes redundancy in aggregated data at CH, reducing resource consumption, leading to energy cost savings, and facilitating the transmission of a compact data stream in the communication bandwidth. The proposed approach shows a 0.606% improvement in network lifetime compared to the approach without compression and 68.01% of energy consumption. Notably, it achieves a reduction of 78.57% in packet loss ratio compared to the state-of-the-art FEEC-IIR model. Thus, the proposed approach shows remarkable improvement in energy-efficient data aggregation with data compression in WSN showcasing its prominence in IoT-based applications.

The increasing complexity and dynamism of modern cyber threats necessitate intelligent and adaptive network intrusion detection systems (NIDS). This paper proposes a novel hybrid metaheuristic approach that combines the Lion Optimization Algorithm (LOA) with the Virus Colony Search (VCS), enhanced by adaptive parameter tuning mechanisms. The proposed LOA-VCS hybrid algorithm addresses limitations in prior single and hybrid metaheuristic by alternating exploration and exploitation strategies across epochs, optimizing detection performance in high-dimensional feature spaces. Unlike previous hybrid metaheuristics that use fixed or non-adaptive control, our model uniquely alternates LOA and VCS phases adaptively across epochs to enhance convergence and detection robustness. A real-world intrusion detection dataset evaluated the LOA-VCS model with 98.4% detection accuracy, an F1-score of 0.976, and an AUC of 0.986, consistently outperforming the standalone LOA and VCS baselines. These results emphasize the power of adaptive hybrid met heuristics in maintaining low false alarms while ensuring strong recall for NIDS. The proposed approach can be deployed in scalable, high-speed systems in today’s contemporary cyber security environments.

With the swift growth of digital networks and information in both public and private sectors, it is essential to deal with the considerable threat that network attacks pose to data integrity and confidentiality. Consequently, there is a pressing requirement for the establishment of effective mechanisms to detect and provide recommendations for addressing intrusion attacks. In this paper, we propose a semantic-based intrusion detection system that aims to improve performance by incorporating semantic representations consisting of feature groups and their associated weights, leading to the creation of a weighted knowledge graph. The weights of the features are determined using sparse autoencoders. From these weights, the most significant features are normalized to a specific range. This approach comprises a combination of a Deep Auto Encoder (AE) and Long Short-Term Memory (LSTM) networks for intrusion detection. Furthermore, the ensemble method of Extreme Gradient Boosting (XGBoost) is used to identify and recommend high-probability attack scenarios. The dataset used to evaluate is the CSE-CIC-IDS dataset. Performance metrics such as accuracy, precision, recall, false positive rate, receiver operating characteristic metrics, loss, and error rate are used to measure the performance, and the results show the approach demonstrates substantial improvements in detection accuracy, minimizing false positives, enhancing reliability, and outperforming existing models. The combination of semantic knowledge, deep learning, and ensemble learning ensures a proactive and adaptive cybersecurity framework.

This paper presents a comprehensive study on the modeling of power line communication (PLC) channels in a MIMO (Multiple Input Multiple Output) environment. PLC systems utilize existing electrical infrastructure to transmit data, but the complexity of these channels, characterized by multipath propagation and inter-symbol interference (ISI), poses significant challenges for designing robust and efficient systems. In this study, we rigorously compare two approaches to PLC channel modelling, which are as follows: the empirical approach and the deterministic approach. The empirical approach relies on the analysis of experimental data to derive statistical models of the channel, offering a realistic representation based on concrete observations. In contrast, the deterministic approach employs theoretical principles and electromagnetic equations to model the channel behavior, providing a detailed description of propagation phenomena. The obtained results demonstrate the significant impact of multipath propagation on the performance of PLC communication systems, highlighting the limitations of empirical models in various scenarios and the increased accuracy of deterministic models. This comparative study conducted in this work highlights the advantages and limitations of each approach and proposes solutions to optimize the performance of power line communication networks.

Wireless Mesh Networks (WMNs) have gained prominence in modern communication technology due to their flexibility and ease of deployment, which are advantageous in scenarios like disaster management and rescue operations. However, existing methods for enhancing the performance of WMNs, such as increasing the number of gateways, are costly, introduce interference, and complicate deployment. Moreover, current routing protocols often suffer from suboptimal packet delivery due to inadequate traffic flow management and packet loss. This research addresses these gaps by proposing a novel optimization model that integrates Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) techniques to enhance packet delivery ratio in WMNs using Voice over Internet Protocol (VoIP). Unlike traditional approaches that overlook efficient traffic management, our proposed model focuses on optimizing packet transmission by selecting efficient routes and minimizing packet loss. The novelty of this solution lies in its hybrid use of ABC and PSO for dynamic node and route selection, which significantly improves network performance, reduces control overhead, and minimizes packet loss. Experimental results demonstrate that the proposed model outperforms existing protocols, making it a promising approach for enhancing network reliability and efficiency in WMNs.

In the pursuit of enhancing Wireless Sensor Networks (WSNs), this study introduces a novel amalgamation of the Enhanced Shuffled Frog Leaping Algorithm (ESFLA) with a multi-solution evolution paradigm. By intricately examining diverse algorithmic facets, including partitioning strategies, fitness functions, and convergence mechanisms, the research endeavors to elevate the efficiency, robustness, and longevity of WSNs. Rigorous experimentation across 15 input datasets, meticulously categorized based on network density, unveils profound insights into the algorithm's performance. Significantly, the proposed ESFLA-MSU achieves exceptional outcomes, eclipsing traditional methods. A pioneering fitness function optimally redistributes workloads, culminating in extended network lifespans, a striking reduction in energy consumption by up to 28.5%, and remarkable load balancing improvements of up to 35.7%. Comparative analyses of partitioning strategies underscore ESFLA's adaptability, while multi-solution evolution integration accelerates convergence, with an expedited rate of up to 46.3%.

This article presents a method for detecting disinformation in news texts based on a combination of classic machine learning algorithms and deep learning models. The proposed approach was tested on the corpus of Ukrainian- and English-language news with the "fake/truth" classes marked. Before modelling, detailed data pre-processing was performed: deletion of duplicates, cleaning of HTML tags, links and special characters, normalisation of texts, unification of labels, class balancing, and tokenisation. A hybrid approach was used for vectorisation: frequency features (TF-IDF) were combined with contextual vector representations based on the IBM Granite multilingual model. Logistic regression is chosen as a classifier, which allows a balance to be achieved between quality and interpretation of results. Standard metrics are used to assess performance, such as Accuracy, Precision, Recall, F1-score, and ROC-AUC. According to the results of experiments, the model showed an Accuracy in the range of 0.91–0.93, a Precision of 0.89, a Recall of 0.92, an F1-score of 0.90, as well as an ROC-AUC over 0.94. The obtained values demonstrate the balanced ability of the system not only to accurately classify news, but also to minimise false positives, which is especially important in the conditions of information warfare. Priority is given to Recall's high scores, as the omission of fake messages can have critical consequences for information security. Thus, the proposed approach makes a scientific contribution to the field of automated disinformation detection by combining transparent and reproducible data processing with a hybrid text representation. The uniqueness of the study lies in the adaptation of NLP and machine learning methods to the Ukrainian-language information space and the context of modern hybrid warfare, which allows you to effectively identify the sources and routes of spreading fake news.