This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.

Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture’s efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model’s decision-making process, supporting future data collection efforts and enhancing security of these systems.

Advances in aerospace engineering and aerodynamics have pioneered space exploration and helped support telecommunication infrastructure. But these same developments have also aided in the creation of weapons of devastating impact. This necessitates the development of ways for detecting and tracking rockets. While several methods, mostly based on Doppler radar exist, the need for active radio emissions limits the applicability of these systems. A passive system has several advantages over traditional techniques, however their potential is largely unexplored. This work seeks to tackle this research gap by exploring the potential of emerging computer vision townies applied to rocket detection and tracking. The advantages of such a system are the relatively low cost as well as passive nature making observation stations harder to detect and easier to deploy. This work explores the potential of pre-trained, lightweight YOLOv8 architectures for rocket detection in real-world situations. A publicly available dataset is utilized and a comparative analysis is carried out between nano and small models. Both models demonstrate favorable outcomes with an accuracy of 0.90 for rocket body detection and 0.93 for engine flame detection. Nevertheless, rocket detection into space is still difficult, with a precision of 0.64 for this class. This paper indicates areas for additional refinement and demonstrates the potential of computer vision technology in passive rocket detection.

The corrosion behavior of AA7049 aluminum alloy in NaCl solution in the presence of environmentally-friendly corrosion inhibitors (Ce-chloride, Ce-formate, Ce-acetate, and Ce-propionate) was analyzed. The chemical composition of intermetallic particles was evaluated by a Scanning Electron Microscope, equipped for Energy Dispersive Spectroscopy measurements, while the presence of cerium and type of functional groups in inhibitors surface layers were analyzed by X-ray Photoelectron Spectroscopy and Fourier-Transform Infrared Spectroscopy, respectively. Inhibitor efficiencies were tested by applying electrochemical techniques such as Electrochemical Impedance Spectroscopy and Linear Sweep Voltammetry. Ce-propionate showed a greater inhibitory effect than all other tested corrosion inhibitors. The inhibitory effect decreases in the following order: Ce-propionate > Ce-acetate > Ce-formate > Ce-chloride. Propionate anions have a higher water contact angle and also a longer aliphatic side chain than acetate and formate anions. The mechanism of inhibitory action of Ce-propionate and other tested corrosion inhibitors has been proposed.

Ventilation mill for grinding coal is one of the main steam power plants in the system that makes a significant effect on the level of energy efficiency with its work. The aim of this paper is to increase the energy efficiency of thermo power plant Kostolac B by reducing the problem of wear in ventilation mill for coal grinding. The idea of this research is to investigate the optimum hardfacing technologies to increase wear resistance of impact plates in ventilation mill. Before applying hardfacing on impact plates in the exploitation conditions, it is necessary to model test hardfaced samples for determination of the optimal technology and filler material for revitalization. The relative weight loss of the impact plates after 1440 h period of exploitation in real conditions is very small, 5–8%. The application of this approach can reduce the number of possible repairs and extends the period between them, resulting in significant economic effects. Before and after revitalization of ventilation mill is conducted analyzing of failure during the working period. In this paper, we present time of repair of ventilation mills number 22, 23, 24 and 25, the number of repairs before and after revitalization and cost of repairs.