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The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks.

This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an individual dataset collected by the authors. Additionally, this paper covers the automatic image text recognition approach using EasyOCR API. Research revealed that the optimal network for segmentation is YOLOv4-tiny, featuring a cross validation accuracy of 96.92%. EasyOCR accuracy was also calculated and is 95.22%.

In recent years, a lot of IoT devices, wireless sensors, and smart things contain information that must be transmitted to the server for further processing. Due to the distance between devices, battery power, and the possibility of sudden device failure, the network that connects the devices must be scalable, energy efficient, and flexible. Particular attention must be paid to the protection of the transmitted data. The Bluetooth mesh was chosen as such a network. This network is built on top of Bluetooth Low-Energy devices, which are widespread in the market and whose radio modules are available from several manufacturers. This paper presents an overview of security mechanisms for the Bluetooth mesh network. This network provides encryption at two layers: network and upper transport layers, which increases the level of data security. The network uses sequence numbers for each message to protect against replay attacks. The introduction of devices into the network is provided with an encryption key, and the out-of-band (OOB) mechanism is also supported. At the moment, a comparison has been made between attacks and defense mechanisms that overlap these attacks. The article also suggested ways to improve network resiliency.

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user&rsquo, s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole.

The number of systems responsible for the processing and transmission of sensory information is steadily growing, which naturally gives rise to the need for a scalable trusted Platform that provides the formation of end-to-end processes in various priority sectors of the economy and social sphere and is an automated information control system for collecting and processing sensory information.

When designing the Platform, which is an automated multilevel system for collecting, processing and presenting sensory information to the user, which ensures the formation of end-to-end processes in various priority sectors of the economy and social sphere, the question of ensuring the information security of the Platform arises. The first stage of solving this issue is not only the implementation of a set of methods, approaches and tools to provide information security, but also their structuring within the framework of the designed Platform. This article discusses the definition of implementation requirements based on an integrated approach that takes into account the requirements of standards and based on an original approach to the classification of information protection mechanisms. The classification approach is based on matching the types of protection mechanisms with the types of security risks relevant to the considered Platform. #CSOC1120.

Information security is one of the most important attributes of distributed systems that often operate on unreliable networks. Enabling security features during the development of a distributed system requires the careful analysis of potential attacks or threats in different contexts, a process often referred to as «threat modeling». Information protection should be comprehensive, but it is also necessary to take into account the possibility of the emergence of threats specific to a certain information system. Many public and private organizations are still trying to implement system models and the threats directed at them on their own. The main reason for this is the lack of useful and high-quality methodologies that can help developers design system models. This review explores a variety of the literature on confidentiality- and integrity-aware system design methodologies, as well as threat classification methods, and identifies key issues that may be referenced by organizations to make design system processes easier. In particular, this article takes a look at the extent to which existing methodologies cover objects of protection and methods of classifying threats, as well as whether there are such models of systems in which the object itself and the threats directed at it are described. This includes whether the compiled models exhibit symmetry or asymmetry. This literature research shows that methodologies appear to be heterogeneous and versatile, since existing methodologies often only focus on one object of protection (a system). Based on the given analysis, it can be concluded that the existing methodologies only relate superficially to the description of system models and threats, and it is necessary to develop a more complete abstract model of the protected object and threats aimed at it in order to make this model suitable for any organization and protect it against most threats.

When building an information security system, one of the key problems is the creation of regulatory documents. Regulators in the field of information security determine the list of necessary documentation mainly in relation to protection mechanisms (authentication, anti-virus protection, etc.) and practically do not take into account the stages of the life cycle of information security tools and personnel of the organization. The article proposes an approach to formalization of the list of information security management processes that need regulation. This approach allows the formation of information security policy to take into account the processes of personnel management and the complex of software and hardware information security, which is necessary to ensure a high level of security of critical information infrastructure.

This paper touches upon a model of simultaneous acoustic masking, which detects speech signal components perceived by a human’s auditory system. A simultaneous masking algorithm on the basis of this model is proposed. It is shown that, after simultaneous masking, a signal becomes a binary structure that reflects the harmonic structure of a vocalized sequence. It is experimentally proven that this structure can be used to detect key speech segments (from the standpoint of perception by an auditory system). This structure serves as a basis for an algorithm of high-quality segmentation of a speech signal into vocalized and unvocalized segments, which does not require learning before use. The joint use of the algorithms for simultaneous masking and speech signal segmentation is tested, and their performance is evaluated.

Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models to detect the attacks in any layer of its architecture. In this regard, minimizing the attacks could be the major objective of cybersecurity, while knowing that they cannot be fully avoided. The number of people resisting the attacks and protection system is less than those who prepare the attacks. Well-reasoned and learning-backed problems must be addressed by the cyber machine, using appropriate methods alongside quality datasets. The purpose of this paper is to describe the development of the cybersecurity datasets used to train the algorithms which are used for building IDS detection models, as well as analyzing and summarizing the different and famous internet of things (IoT) attacks. This is carried out by assessing the outlines of various studies presented in the literature and the many problems with IoT threat detection. Hybrid frameworks have shown good performance and high detection rates compared to standalone machine learning methods in a few experiments. It is the researchers’ recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.

By a model of infringer one means a set of assumptions about the specific (restricted) tools of the infringer, which the latter can use to conduct attacks. The infringer model is an important part of the organization's information security. One should realize that ignoring the model, or building it without due care, can seriously affect the security of confidential information and lead to its loss. The infringer model is informal, which implies the absence of strict and unambiguous methodology for developing such a model. There exist many academic and technical publications proposing various methods of classifying violators. Meanwhile, many information security practitioners are forced to create their own normative and methodological documents, because existing models do not necessarily capture all the aspects of the organization's work. Despite the fact that many models have a high level of correlation between classification characteristics, it has not been possible to work out a unified model so far. We attempt to develop our own methodology for building the infringer model. We have started this project by outlining the roadmap: (1) study the existing methods of constructing the infringer model; (2) identify shortcomings of existing methods; (3) develop a model of the infringer and a methodology for listing the most likely violators, with taking into account the identified shortcomings. In the process of implementation of the plan, we have analyzed several existing models of infringer and revealed their shortcomings and inherent difficulties. In the developed model, causal relationships between the elements of the model and the chains of the alleged consequences have been constructed, and possible types of alleged violators have been described and ranked. As a result, our model allows one to create a more deep description of the infringer.

The presence of imbalance in data significantly complicates the classification task, including fuzzy systems. Due to a large number of instances of bigger classes, instances of smaller classes are not recognized correctly. Therefore, additional tools for improving the quality of classification are required. The most common methods for handling imbalanced data have several disadvantages. For example, methods for generating additional instances of minority classes can worsen classification if there is a strong overlap of instances from different classes. Methods that directly modify the fuzzy classification algorithm lead to a decline in the interpretability of the model. In this paper, we study the efficiency of the gravitational search algorithm in the tasks of selecting the features and tuning the term parameters for fuzzy classifiers of imbalanced data. We consider only data with two classes and apply the algorithm based on extreme values of classes to construct models with a minimum number of rules. In addition, we propose a new quality metric based on the sum of the overall accuracy and the geometric mean with the presence of a priority coefficient between them.

The need to assess the risks of the trustworthiness of counterparties is increasing every year. The identification of increasing cases of unfair behavior among counterparties only confirms the relevance of this topic. The existing work in the field of information and economic security does not create a reasonable methodology that allows for a comprehensive study and an adequate assessment of a counterparty (for example, a developer company) in the field of software design and development. The purpose of this work is to assess the risks of a counterparty’s trustworthiness in the context of the digital transformation of the economy, which in turn will reduce the risk of offenses and crimes that constitute threats to the security of organizations. This article discusses the main methods used in the construction of a mathematical model for assessing the trustworthiness of a counterparty. The main difficulties in assessing the accuracy and completeness of the model are identified. The use of cross-validation to eliminate difficulties in building a model is described. The developed model, using machine learning methods, gives an accurate result with a small number of compared counterparties, which corresponds to the order of checking a counterparty in a real system. The results of calculations in this model show the possibility of using machine learning methods in assessing the risks of counterparty trustworthiness.

As part of the study, the problem of developing and applying a metric for assessing the degree of similarity of time series is considered, which makes it possible to consider the assumptions about the model of a series when comparing, as well as to compare the values of the corresponding characteristics of the series. Characteristics can be values that describe the structure of a series, or directly the values of the approximating function, which can be obtained using nonparametric statistics methods. One of the directions in which this approach can be applied to assessing the similarity of time series is the study of vocal performances. In practice, the degree of similarity in the performance of melodies by several speakers was analyzed. It was determined that, using the synchronicity metric, it is possible to implement an exercise in which students need to repeat the melody after the teacher. In addition, this approach was applied in the segment identification module with an abrupt change in the sounding of the fundamental frequency. This work is devoted to the modification of the program complex for vocal recognition in order to identify notes with a sharp change in the fundamental frequency. The complex is aimed at carrying out additional independent training in teaching vocals. The use of the software package will allow, in real time, providing feedback to the user with an assessment of the quality of their singing. This should allow students to study not only under the supervision of a teacher, but also independently in the early stages of learning. The basic algorithm of the program recognizes notes without sharp changes in frequencies with high accuracy, which is confirmed by experiments. In order to recognize by the algorithms of the program notes sung vibrato and glissando in singing, a new analysis method based on the metric of time series synchronicity is proposed.

This article covers one of the fundamental problems of information security—building a threat model. The article discusses a new method for identifying typical threats to information confidentiality based on the information flow model. The threat model is based on the description of the system. An incorrect description of the system leads to the formation of an incorrect threat model. A review of the subject area revealed several approaches used to describe the system in terms of circulating information flows. Each of these approaches has its own pros and cons. The model of information flows proposed in this work reduces the description of any information system to an eight-digit alphabet. Analysis of the structure of the elementary information flow identified four typical threats to confidentiality, the Cartesian product of a set of threats and a set of streams is a complete model of typical threats to the confidentiality of information processed in cyberspace.

Handwritten signatures are used in authentication systems as a universal biometric identifier. Signature authenticity verification requires building and training a classifier. This paper describes a new approach to the verification of handwritten signatures by dynamic characteristics with a fuzzy rule-based classifier. It is suggested to use the metaheuristic Gravitational Search Algorithm for the selection of the relevant features and tuning fuzzy rule parameters. The efficiency of the approach was tested with an original dataset; the type II errors in finding the signature authenticity did not exceed 0.5% for the worst model and 0.08% for the best model.

The paper discusses methods for constructing threat models of information systems and computer networks. The disadvantages of existing approaches are highlighted. The authors propose an approach to building a computer network model, as well as describing threats to information and the system. The proposed approach takes into account the identified shortcomings of existing solutions and is aimed at reducing the impact of the subjective opinion of an expert when compiling lists of threats.

This article is devoted to studying the possibility of improving the quality of the identification of notes in vocal performance. In the present work, the authors described the transition of the program complex to a distributed functioning model. At this stage, this allowed organizing the storage of results for students and the introduction of an assessment mode for teachers. A study was conducted to improve the quality of note recognition through the use of other algorithms. During the tests, it was determined that the presence of vibrato in singing makes the greatest contribution to the number of unidentified notes. In view of the findings scheduled implementation in a program complex the paalgorithm which is responsible for determining the quality of vibrato in singing. The purpose of this modification is to get rid of novice singers from problems in singing, such as tremor.

The paper describes the implementation of a method for modeling threats to information security using the methodology of IDEF0 functional modeling to solve problems of formalization of specific threat models. The formulation of the method for modeling threats to information security accounts for various media of information transmission and its carriers. In addition, a process approach to information handling is used.

In Tomsk University of Control Systems and Radioelectronics (TUSUR) one of the main areas of research is information security. The work is carried out by a scientific group under the guidance of Professor Shelupanov. One of the directions is the development of a comprehensive approach to assessing the security of the information systems. This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them. The main directions of information security tools development are dynamic methods of biometrics, methods for generating prime numbers for data encryption, steganography, methods and means of data protection in Internet of Things (IoT) systems. The article presents the main results of research in the listed areas of information security. The resultant properties in symmetric cryptography are based on the properties of the power of the generating functions. The authors have obtained symmetric principles for the development of primality testing algorithms, as discussed in the Appendix.

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

Software for speech signal research in patients with malignant diseases of the throat is considered in this work. Basic windows of this system are discussed. These windows represent voice parameters set by a speech therapist to diagnose the disease and to make a decision concerning therapy and rehabilitation.

Existence of the need to assess the quality of computer networks security requires the development of a formalized evaluation method. One of the elements of such method is the model of threats to information system. In the article it is described a model of threats to integrity of information system exposed as a 3-level attributed metagraph. Threat model includes the threats on software, operating system and network layers. The model is used as part of the methodology for assessing the quality of computer network security.

The main elements of a medical hardware complex for speech signal research in patients with phonation disorders are considered. The complex is based on a model of human auditory system. It is shown that the complex can be used for speech recovery and diagnosis of the state of speech production apparatus.