Automated sandbox-based analysis systems are dominantly focused on sequences of API calls, which are widely acknowledged as discriminative and easily extracted features. In this paper, we argue that an extension of the feature set beyond API calls may improve the malware detection performance. For this purpose, we apply the Cuckoo open-source sandbox system, carefully configured for the production of a novel dataset for dynamic malware analysis containing 22,200 annotated samples (11,735 benign and 10,465 malware). Each sample represents a full-featured report generated by the Cuckoo sandbox when a corresponding binary file is submitted for analysis. To support our position that the discriminative power of the full-featured sandbox reports is greater than the discriminative power of just API call sequences, we consider samples obtained from binary files whose execution induced API calls. In addition, we derive an additional dataset from samples in the full-featured dataset, whose samples contain only information on API calls. In a three-way factorial design experiment (considering the feature set, the feature representation technique, and the random forest model hyperparameter settings), we trained and tested a set of random forest models in a two-class classification task. The obtained results demonstrate that resorting to full-featured sandbox reports improves malware detection performance. The accuracy of 95.56 percent obtained for API call sequences was increased to 99.74 percent when full-featured sandbox reports were considered.

Introduction/purpose: Nowadays, user identification systems play a very important role in modern society. Complex security requirements have led experts to explore ways in which biometric data can be used to identify user identities. This paper presents an overview of biometric standards and methods which can be used to identify users in biometric systems, and therefore to protect information and communication systems. Methods: This paper deals with the problem of standardization in the field of biometrics. The first part of the paper presents concrete examples of the most widely used biometric standards. The second part of the paper gives an overview of the most used biometric methods. Results: The obtained results show that the development of biometric systems and biometric sensors contributes to better protection of identity from misuse, because biometric technologies have great potential for improving the security and accuracy of system operation. Biometric systems improve the security of users and also provide much greater precision in establishing identity. Conclusion: The development of biometric standards should focus on their interconnectivity, as well as on increasing connectivity with other IT standards.

As an extension to the concept of the Internet of Things (IoT), Web of Things (WoT) represents a step towards connecting smart things to the existing web environment while considering issues such as heterogeneity, scalability, and usability. This paper is dedicated to current opportunities as well as challenges for development in the concept of WoT. The theoretical foundations of the Internet of Things concept, such as architecture, protocols, services, and things themselves, which form the basis of both concepts, are described in the paper. The paper deals with the necessary preconditions for developing the concept of Web of Things. The main contribution of the paper is a proposal of architecture based on the FIWARE platform as the basis for the development of Web of Things. The demonstration of the proposed architecture is described by a real case scenario.

Biometry is the scientific discipline and technology that measures and analyzes physiological or behavioral characteristics of people and is widely deployed in modern society security systems. Device for biometric identification of maternity is a dual fingerprint scanner that acquires fingerprint templates of the mother and the child at the very moment of birth, generates unique ID reference, and further guarantees mother-child relationship with that reference. Technical issue that is solved with this work and the proposed device is scanning, processing, and storing encrypted biometric templates with a goal to provide a 100% guarantee maternity for each new born child. Scanning the fingerprints of both mother and the child simultaneously, at moment of birth, and pairing them with unique ID reference removes potential fears occurring from hospital negligence to malicious activities, while the data encryption raises the whole process to the highest level of security and confidentiality. The main contribution of the device that removes the fear that almost every mother has in this period as it provides an answer to the question: “Is this my baby?” with a 100% guarantee “It’s certainly yours!”

Introduction/purpose: The process of issuing and retrieving weapons in the military should be fast enough and should provide immediate availability of accurate information on the status of weapons. Methods: This paper deals with the problem of digitizing the recording of issuing and returning weapons through the use of modern Edge computing technology. The problem is presented through two approaches. The first approach is based on the application of machine learning algorithms for recognizing the serial number of a weapon based on the camera image, while the second approach concerns the application of RFID technology. User authentication is based on the application of biometrics. Results: The results obtained from testing the architecture for identifying weapons using a camera indicate that such an architecture is not suitable for identifying weapons. A weapon identification solution using RFID technology overcomes the problems of the previously mentioned solution. However, RFID technology requires additional modifications regarding the implementation of tags on or into weapons so that readings can be made. Conclusion: The implemented weapon identification solution based on RFID technology and a user identification solution with biometric authentication enables easy and reliable identification, speed of issuing and retrieval of weapons, network relieving, and real-time monitoring of the weapon status.

In modern container technologies, Docker is a key tool for deploying and managing applications. Understanding the performance of different Docker networking modes is crucial for efficient system design. This paper analyzes the performance of three core Docker network modes-Bridge, Overlay, and MacvLAN-in a production environment (Docker 27.5.0, Ubuntu 24.04.2 LTS). The methodology involves measuring key performance indicators-latency and throughput-under varying workloads. Results show that MacvLAN, with direct access to the physical interface, delivers the best performance (0.3 ms latency, 9.8 Gbps throughput) but requires strict IP allocation control. Overlay networks, using VXLAN encapsulation, introduce ~1.1 ms additional latency per hop and require careful MTU tuning (≤1400 bytes) to prevent packet fragmentation. Bridge mode shows limitations under high-throughput scenarios due to DNAT processing, leading to ~14% packet loss with a standard MTU of 1500. In conclusion, the optimal Docker network mode depends on workload characteristics: MacvLAN is ideal for latency-sensitive applications, Overlay suits distributed systems with optimized MTU settings, and Bridge is best for development environments when paired with an external load balancer.