The amount of fraud on the Internet is increasing along with the availability and the popularity of the Internet around the world. One of the most common forms of Internet fraud is phishing. Phishing attacks seek to obtain a user’s personal or secret information. The variety of phishing attacks is very broad, and usage of novel, more sophisticated methods complicates its automated filtering. Therefore, it is important to form up-to-date and detailed phishing attack taxonomy, which could be used for both human education purposes as well as phishing attack discrete notation. In this paper, we propose an e-mail-based phishing attack taxonomy, which includes six phases of the attack. Each phase has at least one criterion for the attack categorization. Each category is described, and in some cases the categories have sub-classes to present the full variety of phishing attacks. The proposed taxonomy is compared to similar taxonomies. Our taxonomy outperforms other phishing attack taxonomies in numbers of phases, criteria and distinguished classes. Validation of the proposed taxonomy is achieved by adapting it as a phishing attack notation for an incident management system. Taxonomy usage for phishing attack notation increases the level of description of phishing attacks compared to free-form phishing attack descriptions.

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

The development of information technologies changes the learning process. The amount of publicly available data of e-learning systems allows personalized studies. Therefore, the tutor sometimes is needed for the student’s evaluation and consultation only. To ensure clear evaluation requirements and objective evaluation process, the learning material, as well as the evaluation system, must be discrete and semantically expressed. The list of mastered competencies and skills is more important to the enterprise; therefore, during the last years, the study process has concentrated on competency evaluation too. However, the current practice, when students’ competencies are summarized and expressed as one quantitative metric (score), do not express the list of students’ competencies and their level. To solve the problem, in this paper, we proposed a method for the design of competencies’ tree. The competency tree has to be formatted based on context modeling principles and analysis of Scope-Commonality-Variability. The usage of competency tree for students’ competencies’ evaluation proposes clearly defined and semantically expressed evaluation method for both human and e-learning evaluation process. This paper presents the results of the empirical experiment to adapt the proposed competency tree design and application for competencies’ e-evaluation method, based on flexibility, adaptability, and granularity of learning material.

Simulation-based e-learning is recently budding demand for the next generation of e-learning. Learning about dynamic phenomena is essential in many domains. That approach allows the student to imitate real processes using models and to obtain knowledge and experience during the process of learning. The proposed graphical modelling method is based on systematic point of view, experiential learning and expert-based modelling requirements. The contextual graph of action specification is used as a basis to set the requirements for service software specification and attributes of learning objects (LO). The paper presents the enhanced architecture of the student self-evaluation and on-line assessment system TestTool (TT). The system is explored as an assessment engine capable of supporting and improving the individualized self-instructional and simulation-based mode of learning, grounded on the GRID distributed service architecture. This architecture, services and method are being examined in study modules for Kaunas University of Technology (KTU) students and some organizations.

Software code testing remains a critically important but labour-intensive process in software quality assurance. Existing research evaluates large language model (LLM)-based unit test generation using various quality metrics, such as correctness, coverage, mutation score, and test code smells. However, these single metrics do not reflect the trustworthiness of the unit test generation process. Therefore, this research formalises the trustworthiness of LLM-based unit test generation as a multidimensional index comprising reliability, hallucination resistance, maintainability, functional completeness, and human-reference alignment. In this research, we investigate the effect of prompt engineering strategies on the trustworthiness of LLM-generated unit tests and compare them with human-written tests for the same focal methods. Each dimension is fed by a distinct artefact-level measurement and grounded in dependability theory and ISO/IEC 25010:2023. A centralised multi-agent system generates, builds, repairs, and measures the tests, so that all inputs are collected automatically. The index is evaluated on real-world C# focal methods across 18 model × prompt configurations and a paired human-written baseline. The human baseline achieves the highest T-UTG value (0.904), and the best configuration, Combined × Gemini, achieves 0.788. Entropy weighting identifies maintainability and hallucination resistance as the most discriminating dimensions, and a rank-acceptability analysis over the whole weight simplex confirms that this ordering does not depend on the chosen weighting scheme.

Security defects in software code can lead to situations that compromise web-based systems, data security, service availability, and the reliability of functionality. Therefore, it is crucial to detect code vulnerabilities as early as possible. During the research, the architectures of the deep learning models, peephole LSTM, GRU-Z, and GRU-LN, their element regularizations, and their hyperparameter settings were analysed to achieve the highest performance in detecting SQL injection vulnerabilities in Python code. The results of the research showed that after investigating the effect of hyperparameters on Word2Vector embeddings and applying the most efficient one, the peephole LSTM, delivered the highest performance (F1 = 0.90)—surpassing GRU-Z (0.88) and GRU-LN (0.878)—thereby confirming that the access of the peephole connections to the cell state produces the highest performance score in the architecture of the peephole LSTM model. Comparison of the results with other research indicates that the use of the selected deep learning models and the suggested research methodology allows for improving the performance in detecting SQL injection vulnerabilities in Python-based web applications, with an F1 score reaching 0.90, which is approximately 10% higher than achieved by other researchers.