Traffic safety in railway transport requires regular inspection of the condition of rails to identify and timely eliminate defects. Eddy-current flaw detection is one of the popular methods of nondestructive rail testing. The data (defectograms) obtained from eddy-current flaw detectors during rail testing are typically high-volume and therefore require efficient automated analysis. In this context, analysis means the process of detecting the presence of defective areas on defectograms, as well as identifying the structural elements of the rail track, taking into account noise and various types of interference. The threshold noise level is found to isolate signals from defects and structural elements. Its value can be distorted by electromagnetic influences superimposed on the signals, which have pronounced low frequency and periodicity. This interference raises the threshold noise level, complicating the detection of useful signals. In relation to this, these types of effects need to be suppressed. In this paper spectral subtraction is used as a method for interference reduction in eddy-current defectograms. The interference function is defined as the sum of the low-frequency harmonics of the discrete Fourier transform of the original signals. Then the cleaned signal can be found by subtracting the low-frequency range. The upper boundary of this range is referred to as the threshold harmonic frequency. This frequency is determined by minimizing the distance of the signal’s autocorrelation function and expected autocorrelation. Two types of expected correlation are proposed: Gaussian noise autocorrelation and the reference autocorrelation. Both approaches allow us to determine the threshold harmonic frequency at which periodic interference is most effectively suppressed. The method based on Gaussian noise autocorrelation method is, to some extent, universal for eddy-current defectograms, while the reference autocorrelation is data- and recording equipment-specific. The most suitable threshold harmonic frequency is found for the eddy-current defectogram data under consideration. The described approaches to suppressing periodic low-frequency interference may also be successfully applied in other fields beyond eddy-current testing.

The exponential growth of scientific publications has heightened the need for robust tools to organize and retrieve research effectively. The Universal Decimal Classification (UDC) serves as a valuable framework for categorizing articles by subject area. However, manual assignment of UDC codes is often prone to inaccuracies or oversimplification, limiting its utility. In this study, we present a novel approach for the automated assignment of UDC codes to scientific articles using BERT-based models. Our methodology is trained and evaluated on a dataset comprising over 19 000 articles in mathematics and related disciplines. To address the hierarchical structure of the UDC, we develop two specialized evaluation metrics: hierarchical classification accuracy and hierarchical recommendation accuracy. We also explore multiple strategies for flattening hierarchical labels. Our results demonstrate a hierarchical recommendation accuracy of 0.8220. Furthermore, blind expert evaluation reveals that discrepancies between the reference and predicted labels often stem from errors in the original UDC code assignments by the authors of articles. Our approach demonstrates strong potential for automating the classification of scientific articles and can be extended to other hierarchical classification systems.

Interacting in open networks carries certain risks. To ensure the information security of network interaction participants, cryptographic protocols (CrPs) are used. High levels of security can be achieved through their formal verification. A common formal method for verifying the CrP is model checking. In this study, we propose using the TLA+/TLC toolset to check models of CrP. This toolset is widely applied in various practical fields. In the TLA+ specification language, the protocol model and the required security properties in the form of invariants are defined. The protocol model describes its behavior as a transition system containing all possible states of the protocol model and transitions between them. The TLC model checker is employed to automatically verify that the model meets the required properties. The task of verifying the CrP has its specifics. This study proposes three modeling techniques that take into account the specifics of both the task and the TLA+/TLC toolset being used. The first technique involves replacing a system consisting of an arbitrary number of agents with a three-agent system. This simplifies the model and reduces its state space. The second technique is related to representing transmitted messages as a hierarchical structure, allowing encrypted messages to be nested within others. The third technique consists of optimizing the model to improve the performance of the TLC model checker by defining a function that generates only those elements leading to transitions between states in the model. These techniques simplify the model and reduce the verification time. We demonstrate the application of these results on a simple protocol example—the Needham–Schroeder (NS) public-key authentication protocol. After detecting a known vulnerability in the original protocol by using TLC, we model and verify an improved version. The verification results show that the new version of the protocol does not have this vulnerability.

Picking a subset of possible features is a vital step in the data-mining procedure. The decisive goal of feature-selection is to determine the optimal number of superior characteristics to make best use of the presentation of the learning algorithm. However, this problem becomes increasingly challenging to resolve as the number of features in a data set increase. Therefore, to identify the optimal feature combinations, contemporary optimization techniques are used. Numerous optimization problems have been successfully resolved using the innovative metaheuristic known as the marine predators algorithm (MPA). Support vector machines (SVMs) are a crucial technique that is expertly applied to classification issues. In this work, the issue of feature picking in large dimensional data sets is solved by adjusting the MPA using the SVM as a classifier. In order to address the problem of feature selection in large dimensional data sets, the current study suggests MPA + SVM. Ten high-dimensional data sets got from the Arizona State University (ASU) source were employed to prove the usefulness of the planned method; the outcomes are likened with those of the additional six cutting-edge picking features algorithms. We compared the following algorithms: atom search optimization (ASO), satin bowerbird optimizer (SBO), emperor penguin optimizer (EPO), equilibrium optimizer (EO), monarch butterfly optimization (MBO), and sine cosine algorithm (SCA). The consequences confirm that the planned MPA+SVM approach outperformed several metaheuristic algorithms and presented a remarkable capability to pick the utmost weighty and optimum features. MPA + SVM yields the lowermost averaged error rates, minimal classification standard deviation (STD) values, and FS rates across all data sets.

The features of mobile ad hoc networks are examined. The models of node interaction in these networks are analyzed, taking into account protection against network attacks, and their advantages and disadvantages are identified. A model of node interaction in a mobile ad hoc network is proposed, taking into account protection against active network attacks based on early attack detection. Early detection of network attacks is achieved by predicting the network parameters and further analyzing them using machine learning methods. A trust model is also used to exclude malicious nodes from the network.

This article continues the series of works on the development and verification of control programs based on LTL (linear temporal logic) specifications of a special type. Previously, a declarative LTL specification was proposed, which allows describing the behavior of control programs and building program code based on it in the imperative ST language for programmable logic controllers (PLCs). This LTL specification can be directly verified for compliance with specified temporal properties by the model checking method using the nuXmv symbolic verification tool. In general, it is not required to translate the LTL formulas of the specification into another formalism—an SMV specification (code in the input language of the nuXmv tool). The aim of this study is to explore alternative ways of representing the behavioral model of a program that conforms to a declarative LTL specification during its verification using the nuXmv tool. In this article, we transform the declarative LTL specification into various SMV specifications with accompanying changes of formulation of the verification problem, which leads to a significant reduction in time costs when checking temporal properties by using the nuXmv tool. The acceleration of verification is due to the reduction of the state space of the model being verified. The SMV specifications obtained as a result of the proposed transformations specify identical or bisimulational-equivalent transition systems, ensuring the same verification results when replacing one SMV specification with another.

In [1], two new approaches to key pre-distribution based on an ideal additive homomorphic secret sharing scheme (SSS) are proposed. However, it has not been possible to prove their security against insider attacks in the general case. In this study, a simple method for distributing shares, corresponding to the first approach in [1], is proposed and analyzed using Shamir’s SSS. The problem considered is the distribution of 2n shares among n participants in such a way that each participant holds two shares, and every pair of participants corresponds to a (3,4)-threshold scheme, while the overall threshold can be arbitrary. It should be noted that this is the first time such a problem is solved in the theory of secret sharing. The analysis shows that the key agreement protocol based on the proposed technique of shares pre-distribution is not resistant to insider attacks. A general necessary condition for the security of a key agreement protocol in the internal adversary model is obtained.

The main biometric characteristics reflecting changes in the psycho-emotional state of a user of an information system are examined. They are ranked using the pairwise comparison method, as a result of which voice and keystroke dynamics are identified as most suitable for further research. Criteria for the preliminary identification of potential internal information security violators based on changes in the considered biometric characteristics are defined. A convolutional neural network model is developed and tested to solve the stated problem.

With the widespread operation of high-speed trains, traction motors are susceptible to failures under high-temperature conditions and complex working scenarios. This leads to abnormal temperature increases, which pose significant threats to train safety. Therefore, researching the temperature prediction of traction motors is of great significance for ensuring the safe operation of trains. This paper proposes a temperature prediction method based on an improved bidirectional long short-term memory (Bi-LSTM) network. Initially, convolutional neural networks (CNN) are employed to extract temporal structures from the data. Simultaneously, the squeeze-and-excitation (SE) attention mechanism is integrated with the key-query-value (KQV) mechanism. Finally, Bi-LSTM is utilized for temperature prediction. The proposed SE-BiLSTM-CNN model exhibits significantly enhanced prediction performance compared with recurrent neural networks (RNN), gated recurrent units (GRU), LSTM, and Bi-LSTM models, with improvements of 35.51, 56.07, and 33.75% in the evaluation metrics of MAE, MSE, and RMSE, respectively.

Existing methods for detecting attacks on data integrity in file systems are studied. A detection method based on the use of several entropy metrics is proposed. The effectiveness of the proposed method is evaluated using the example of detecting existing ransomware programs.