In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The main objective of self-organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM-Toolbox, Databionic ESOM and Viscovery SOMine) have been investigated. Each system has its additional tools for visualizing SOM. A comparative analysis has been made for two data sets: Fisher’s iris data set and the economic indices of the European Union countries. A new SOM system is also introduced and researched. The system has a specific visualization tool. It is missing in other SOM systems. It helps to see the proportion of neurons, corresponding to the data items, belonging to the different classes, and fallen in the same SOM cell.

In today’s cyber environment, threats such as data breaches, cyberattacks, and unauthorized access threaten national security, critical infrastructure, and financial stability. This research addresses the challenging task of protecting critical infrastructure from insider threats because of the high level of trust and access these individuals typically receive. Insiders may obtain a system administrator’s password through close observation or by deploying software to gather the information. To solve this issue, an innovative artificial intelligence-based methodology is proposed to identify a user by their password’s keystroke dynamics. This paper also introduces a new Gabor Filter Matrix Transformation method to transform numerical values into images by revealing the behavioral pattern of password typing. A siamese neural network (SNN) with the branches of convolutional neural networks is utilized for image comparison, aiming to detect unauthorized attempts to access critical infrastructure systems. The network analyzes the unique features of a user’s password timestamps transformed into images and compares them with previously submitted user passwords. The obtained results indicate that transforming the numerical values of keystroke dynamics into images and training an SNN leads to a lower equal error rate (EER) and higher user authentication accuracy than those previously reported in other studies. The methodology is validated on publicly available keystroke dynamics collections, the CMU and GREYC-NISLAB datasets, which collectively comprise over 30,000 password samples. It achieves the lowest EER value of 0.04545 compared to state-of-the-art methods for transforming non-image data into images. The paper concludes with a discussion of findings and potential future directions.

In the paper, an overview of methods and technologies used for big data clustering is presented. The clustering is one of the important data mining issue especially for big data analysis, where large volume data should be grouped. Here some clustering methods are described, great attention is paid to the k-means method and its modifications, because it still remains one of the popular methods and is implemented in innovative technologies for big data analysis. Neural network-based self-organizing maps and their extensions for big data clustering are reviewed, too. Some strategies for big data clustering are also presented and discussed. It is shown the data of which volume can be clustered in the well known data mining systems WEKA and KNIME and when new sophisticated technologies are needed.