Graph Visualization: Alternative Models Inspired by Bioinformatics.

Currently, the methods and means of human-machine interaction and visualization as its integral part are being increasingly developed. In various fields of scientific knowledge and technology, there is a need to find and select the most effective visualization models for various types of data, as well as to develop automation tools for the process of choosing the best visualization model for a specific case. There are many data visualization tools in various application fields, but at the same time, the main difficulty lies in presenting data of an interconnected (node-link) structure, i.

Cyber Attacker Profiling for Risk Analysis Based on Machine Learning.

The notion of the attacker profile is often used in risk analysis tasks such as cyber attack forecasting, security incident investigations and security decision support. The attacker profile is a set of attributes characterising an attacker and their behaviour. This paper analyzes the research in the area of attacker modelling and presents the analysis results as a classification of attacker models, attributes and risk analysis techniques that are used to construct the attacker models.

Characteristics of salivary telomere length shortening in preterm infants.

Objective: To examine the association between gestational age, telomere length (TL) and rate of shortening in newborns.

Study Design: Genomic DNA was isolated from buccal samples of 39 term infants at birth and one year and 32 preterm infants at birth, term-adjusted age (40 weeks post-conception) and age one-year corrected for gestational duration. Telomere length was measured by quantitative real-time PCR.

A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods.

The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating its self-similarity, detecting and classifying cyberattacks in anomalies, and taking effective protection measures using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Fractal analysis, mathematical statistics, and neural networks with long short-term memory are used as tools in the development of this protection system.

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer's Point of View.

Nowadays, the whole driver monitoring system can be placed inside the vehicle driver's smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area.