Lyudmila Sukhostat

• Doctor of Philosophy
• chief researcher at Institute of Information Technology

Timely identification of critical security flaws in a cyber-physical system makes identifying risks and potential threats possible. To address this issue, threat models are created to better understand potential vulnerabilities that must be considered to ensure system reliability. Selecting the optimal solution for assessing the functional vulnerab...

In recent years, the amount of data created worldwide has grown exponentially. The increase in computational complexity when working with "Big data" leads to the need to develop new approaches for their clustering. The problem of massive data amounts clustering can be solved using parallel processing. Dividing the data into batches helps to perform...

Computer networks are getting more complex these days. A computer network failure can result in the loss of important data, disruption of network services and applications, and economic loss and threaten national security. Therefore, it is crucial to detect failures on time and diagnose their root cause, which is possible with the help of proactive...

An approach based on a hierarchical hidden Markov model for anomaly detection in industrial control systems is proposed. The signals of the system components are fed to the input of the proposed model. The hidden state is an independent probabilistic model, so each state is also a hidden Markov model. In the proposed model, the detection of anomali...

This paper proposes a novel approach based on deep learning to improve oil reservoirs' history matching problem. Deep autoencoders are widely used to solve the oil industry problems. However, as the input data increases, the autoencoder parameters increase exponentially. Our model is based on a convolutional variational autoencoder using AlexNet an...

This paper proposes a machine learning method for automatically detecting and identifying facies from digital images of the oil well core. The method is based on artificial neural networks, specifically, pre-trained deep convolutional neural networks, improved using histogram of oriented gradients and local binary pattern methods. AlexNet, Inceptio...

Proxy-model is a popular reservoir modeling tool in the oil and gas industry due to its computational efficiency. This paper proposes and evaluates a proxy-model for reservoir history matching using extreme learning machines. The model does not require many computational resources when it is necessary to perform a large number of iterations. The pr...

Careful monitoring of plant conditions and their diagnosis are necessary, but a human cannot control a large area of land where the crop grows. This paper proposes the solution of this problem. Early diagnosis and accurate detection of plant leaf diseases can prevent the spread of the disease. In the last decade, machine learning methods and image...

Context. The problem of detecting anomalies from signals of cyber-physical systems based on spectrogram and scalogram images is considered. The object of the research is complex industrial equipment with heterogeneous sensory systems of different nature. Objective. The goal of the work is the development of a method for signal anomalies detection b...

The urgency of solving the problem of ensuring the security of cyber-physical systems is due to ensure their correct functioning. Cyber-physical system applications have a significant impact on different industrial sectors. The number and variety of cyber-attacks are growing, aimed not only at obtaining data from cyber-physical systems but also man...

The application of clustering algorithms is expanding due to the rapid growth of data volumes. Nevertheless, existing algorithms are not always effective because of high computational complexity. A new parallel batch clustering algorithm based on the k-means algorithm is proposed. The proposed algorithm splits a dataset into equal partitions and re...

Performance assessment and timely failure detection of the electric submersible pump can reduce operation costs and maintenance in the oil and gas field. Features of equipment malfunction are changes in vibration signals. Evaluation of vibrations based on accelerometer sensors can detect failures and allows assessment of system failures. This paper...

The aim of this study is the development of a weighted consensus clustering that assigns weights to single clustering methods using the purity utility function. In the case of Big data that does not contain labels, the utility function based on the Davies-Bouldin index is proposed in this paper. The Banknote authentication, Phishing, Diabetic, Magi...

Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth of computations. Th...

The aim of this paper is the development of an effective model based on deep learning for geological facies classification in wells. Facies classification is carried out by studying the lithological properties of rocks, which are characteristic of modern sediments, accumulating in certain physical and geographical conditions. In this study, a new 1...

Context. The task of using the ensemble of classifiers to detect DoS attacks in large arrays of network traffic data is solved to withstand attacks on the network. Objective of this paper is to build an ensemble of classifiers that surpasses single classifiers in terms of accuracy. Method. To achieve the formulated goal an algorithm, that indicates...

The creation of cyber-physical systems posed new challenges for people. Ensuring the information security of cyber-physical systems is one of the most complex problems in a wide range of defenses against cyber-attacks. The aim of this paper is to analyse and classify existing research papers on the security of cyber-physical systems. Philosophical...

The creation of cyber-physical systems posed new challenges for people. Ensuring the information security of cyber-physical systems is one of the most complex problems in a wide range of defenses against cyber-attacks. The aim of this paper is to analyse and classify existing research papers on the security of cyber-physical systems. Philosophical...

In this paper, a new method for anomaly detection based on weighted clustering is proposed. The weights that were obtained by summing the weights of each point from the data set are assigned to clusters. The comparison is made using seven datasets (of large dimensions) with the k-means algorithm. The proposed approach increases the reliability of d...

At present, an anomaly detection is one of the important problems in many fields. The rapid growth of data volumes requires the availability of a tool for data processing and analysis of a wide variety of data types. The methods for anomaly detection are designed to detect object's deviations from normal behavior. However, it is difficult to select...

Selection of the right tool for anomaly (outlier) detection in Big data is an urgent task. In this paper algorithms for data clustering and outlier detection that take into account the compactness and separation of clusters are provided. We consider the features of their use in this capacity. Numerical experiments on real data of different sizes de...

Актуальность. Решена актуальная задача оценки информативности признаков данных большой размерности. Объектом исследования являлся сетевой трафик.Цель работы – анализ данных сетевого трафика на предмет информативности для выявления аномалий в сетевом трафике с целью сокращения пространства признаков.Метод. Предложен подход для оценки информативности...

Pitch is one of the most important components in various speech processing systems. The aim of this study was to evaluate different pitch detection methods in terms of various noise conditions. Prospective study. For evaluation of pitch detection algorithms, time-domain, frequency-domain, and hybrid methods were considered by using Keele and CSTR s...

Pitch period evaluation of speech signal is used in many important applications of speech technology. However, among the existing methods only some can work in case of non-linear and non-stationary signals. The main reason is that the pitch detection methods are based on the assumption that speech production process is linear. Selection of pitch pe...

This paper discusses various methods for channel compensation to effectively reduce errors in speech data transmitted through different channels in order to increase the accuracy of speaker recognition system. The aim of this paper is to analyze and evaluate the channel distortions and noise effects on speech. Methods used for channel equalization...

Speech signal contains information not only connected to the pronounced phrase, but also data about speaker, language, environment, emotional state of the speaker. The main objective of the research is development of methods and algorithms increasing the precision of speaker recognition preserving acceptable indicators on computational complexity....

Combining two known technologies—biometric and cryptographic—is an urgent problem. The main line of research in this direction is the use of biometric technologies for the control of cryptographic keys. In the present work, a method for the compact representation of fingerprints is proposed, and a method for cryptographic key generation on this bas...

The recognition of vowels in Azerbaijani speech is very important for Azerbaijani speakers. However, it is rather difficult and there has been no efficient method to solve it yet. In this paper, we propose an approach to the recognition of Azerbaijani vowels via the support vector machine (SVM) with the Mel-Frequency Cepstral Coefficients (MFCCs) a...

Annotation. Use of fuzzy integrals is proposed for aggregation of classifiers results in multi-biometric systems. It is significantly better than application of a single classifier. Also, advantages and disadvantages of application of fuzzy integral method are reviewed.