COVID-19 forced a number of changes in many areas of life, which resulted in an increase in human activity in cyberspace. Furthermore, the number of cyberattacks has increased. In such circumstances, detection, accurate prioritisation, and timely removal of critical vulnerabilities is of key importance for ensuring the security of various organisations. One of the most-commonly used vulnerability assessment standards is the Common Vulnerability Scoring System (CVSS), which allows for assessing the degree of vulnerability criticality on a scale from 0 to 10. Unfortunately, not all detected vulnerabilities have defined CVSS base scores, or if they do, they are not always expressed using the latest standard (CVSS 3.x). In this work, we propose using machine learning algorithms to convert the CVSS vector from Version 2.0 to 3.x. We discuss in detail the individual steps of the conversion procedure, starting from data acquisition using vulnerability databases and Natural Language Processing (NLP) algorithms, to the vector mapping process based on the optimisation of ML algorithm parameters, and finally, the application of machine learning to calculate the CVSS 3.x vector components. The calculated example results showed the effectiveness of the proposed method for the conversion of the CVSS 2.0 vector to the CVSS 3.x standard.

In this study, we presented the concept and implementation of a fully functional system for the recognition of bi-heterocyclic compounds. We have conducted research into the application of machine learning methods to correctly recognize compounds based on THz spectra, and we have described the process of selecting optimal parameters for the kernel support vector machine (KSVM) with an additional ‘unknown’ class. The chemical compounds used in the study contain a target molecule, used in pharmacy to combat inflammatory states formed in living organisms. Ready-made medical products with similar properties are commonly referred to as non-steroidal anti-inflammatory drugs (NSAIDs) once authorised on the pharmaceutical market. It was crucial to clearly determine whether the tested sample is a chemical compound known to researchers or is a completely new structure which should be additionally tested using other spectrometric methods. Our approach allows us to achieve 100% accuracy of the classification of the tested chemical compounds in the time of several milliseconds counted for 30 samples of the test set. It fits perfectly into the concept of rapid recognition of bi-heterocyclic compounds without the need to analyse the percentage composition of compound components, assuming that the sample is classified in a known group. The method allows us to minimize testing costs and significant reduction of the time of analysis.

In the context of vulnerability management for data communication networks, determining which vulnerabilities to address first is of paramount importance. While identifying vulnerabilities using network scanners is relatively straightforward, efficiently prioritizing them for mitigation remains a significant challenge. Previously, our team developed a machine learning-based converter to translate CVSS v2.0 base scores into CVSS v3.x base scores, specifically to enable the use of the CVSS v3.x environmental score. The central question of this research is whether leveraging these converter-enabled CVSS v3.x environmental scores leads to a measurably improved vulnerability prioritization process compared to traditional methods, often reliant solely on CVSS v2.0 base scores. The environmental score potentially offers a more refined, context-specific perspective on vulnerability impact within specific systems. To evaluate this approach, we will test the converter’s performance in real-world environments and assess its impact on network administrator decision-making and workflows. Performance improvement will be measured by analyzing changes in mitigation times, potential threat exposure reduction, and overall vulnerability management efficiency. The ultimate goal is to determine if the proposed machine learning based methodology delivers practical benefits, enhancing organizational security through more accurate and effective vulnerability prioritization.Experimental results demonstrate that CVSS v3.x environmental scoring resolves critical v2.0 imprecision issues, enabling more accurate vulnerability prioritization. Our approach achieves measurable efficiency gains, reducing estimated remediation work hours by up to 8% compared to CVSS v2.0 methods. The study confirms that the proposed methodology delivers practical benefits, enhancing organizational security through more accurate and effective vulnerability prioritization.

In this paper, a simple and fast method relied upon for designing a Vlasov launcher with a helical cut is proposed. The method is based on graphic interpretation of analytical relationships that link wave parameters (EM field mode) to the launcher’s geometrical dimensions. Using the ray tracing method, a simplified graphic analysis may be carried out. The results obtained are not significantly different from those of rigorous full-wave analyzes. The family of normalized curves that is created in the process greatly facilitates the stage of optimizing the geometrical parameters of the Vlasov launcher

This survey presents a review of experimental methods relied upon while implementing gyrotron higher mode generation techniques and performing near electromagnetic field measurements in launcher and quasi-optical mode converters. In particular, the paper focuses on low power (cold) testing of gyrotron quasi-optical mode converters outside of the gyrotron, without the presence of high electromagnetic power and electron beams