Ship detection and tracking is a basic task in any vessel traffic monitored area, whether marine or inland. It has a major impact on navigational safety and thus different systems and technologies are used to determine the best possible methods of detecting and identifying sailing units. Video monitoring is present in almost all of them, but it is usually operated manually and is used as a backup system. This is because of the difficulties in implementing an efficient and universal automatic detection method that would work in quickly alternating environmental conditions for all kind of sailing units—from kayaks to seagoing merchant vessels. This paper presents a method that allows the detection and tracking of ships using the video streams of existing monitoring systems for ports and rivers. The method and the results of experiments on three sets of data using cameras with different characteristics, settings, and scene locations are presented. The experiments were carried out in variable light and weather conditions, and a wide range of unit types were used as detection objectives. The results confirm the usability of the proposed solution; however, some minor issues were encountered in the presence of ships wakes or highly unfavourable weather conditions.

(1) Background: Large eHealth systems should have a mechanism to detect unauthorized changes in patients’ medical documentation, access permissions, and logs. This is due to the fact that modern eHealth systems are connected with many healthcare providers and sites. (2) Methods: Design-science methodology was used to create an integrity-protection service model based on blockchain technology. Based on the problem of transactional transparency, requirements were specified and a model was designed. After that, the model’s security and performance were evaluated. (3) Results: a blockchain-based eHealth integrity model for ensuring information integrity in eHealth systems that uses a permissioned blockchain with off-chain information storage was created. In contrast to existing solutions, the proposed model allows information removal, which in many countries’ eHealth systems is a legal requirement, and is based on a blockchain using the Practical Byzantine Fault Tolerant algorithm. (4) Conclusion: A blockchain can be used to store medical data or only security-related data. In the proposed model, a blockchain is mainly used to implement a data-integrity service. This service can be implemented using other mechanisms, but a blockchain provides a solution that does not require trusted third parties, works in a distributed eHealth environment, and supports document removal.

The identification of ships plays a crucial role in security and managing vessel traffic for ports and onshore facilities. Existing video monitoring systems help visually identify a vessel where other systems are not present or sufficient. Readable vessel plates and hull inscriptions of detected ships in the video stream allow using text location and recognition methods to obtain ships’ identification names or numbers. The obtained information can be then matched with available ship registers. The automation of the process has met many challenges related to the often-low quality of available video streams, heterogeneous regulations on the marking of ships, and the specifics of natural scene text recognition, such as quickly alternating imaging conditions or the interference of the background. The main contribution of this research is a method that can identify any type of vessel in an image that has visible inscriptions (name, registration number) placed on the hull and must be registered in a public registry. The proposed method works with low-quality images with inscriptions placed under different angles and different, readable sizes. Our method recognised 91% of vessels from our test dataset. Obtained identification times have not exceeded 1s. The quality and efficiency of the proposed solution indicate that it is suitable for practical implementation in onshore monitoring systems.

This article analyses the performance characteristics of XGBoost models across multiple datasets for phishing URL detection, extending our previous conference paper with comprehensive cross-dataset validation and comparative analysis. Using a validation framework with three distinct datasets — a custom phishing dataset (75,738 samples), the large-scale GramBeddings dataset (639,723 samples), and the PhiUSIIL dataset (47,103 samples)—we demonstrate that XGBoost delivers robust performance across diverse data sources. Our approach addresses the issue of feature instability, showing how balanced feature engineering is crucial for reliable detection. The model achieves 90.7% accuracy and 91.2% F1 score on the custom dataset, with a solid 77.8% average accuracy across three independent test sets. In comparative benchmarks against Neural Networks, XGBoost proved superior in training efficiency and detection quality, achieving 2.1% higher accuracy and training 12.6 times faster (0.95s vs 12.01s) with 3.6 times less memory. Although Neural Networks offered faster inference (7.51ms vs 20.6ms) and smaller model sizes, XGBoost’s balance of high accuracy and rapid training makes it highly effective for practical, real-world phishing detection systems.

The goal of this study is to measure the cyber security awareness of medical professionals in Poland, i.e. to verify whether healthcare specialists have knowledge and understanding of basic cyber security threats. This survey was based on the cyber security recommendations from the European Union Agency for Network and Information Security and the U.S. Department of Health and Human Services. The survey consisted of 23 single and multiple-choice questions divided into four parts. The results categorized the respondents and measured the level of cyber security awareness. Among the 620 persons invited to participate in the survey, 300 (48.39%) responded and answered all of the questions. The results show a an unsatisfactory level of knowledge regarding information security in Poland. The main conclusion drawn from the survey is that the quality of cyber security training among medical professionals should be improved and frequency of the trainings should be increased.