The spread of malicious information on social networks is often explosive and difficult to control, especially in small-world structures and echo chambers. Traditional intervention strategies based on degree centrality are effective in scale-free networks; however, they often perform poorly in relatively homogeneous community networks, where bridge nodes may be more important than hubs. In this paper, we propose SPID (Shortest-Path Interdiction for Diffusion), a corridor-based mitigation framework that targets fast-spreading “diffusion corridors”, defined as shortest paths connecting high-risk source regions to target communities. The experimental implementation uses SPID++, a residual-updated version of SPID that adapts to rerouted paths after each intervention step. Experiments on the Watts–Strogatz network show that SPID++ reduces the total number of infected nodes by approximately 2.2 times compared with Degree Centrality. Budget sensitivity analysis further shows that SPID++ can protect up to 444 more nodes than Degree as the intervention budget increases. Runtime results indicate that batched shortest-path computation reduces the intervention-selection time of SPID++ while preserving the selected intervention set.

The challenge of finding the shortest path in graphs containing cycles and negative weights presents substantial difficulties across multiple fields, including transportation, social network analysis, and the study of complex systems. While sequential algorithms provide effective solutions for small to medium-sized graphs, they become impractical for large graphs due to constraints in processing time and computational resources. This paper presents an extension of the shortest path algorithm into a parallel computing environment, leveraging the power of modern multicore systems. Experimental results demonstrate that the parallel version achieves superior performance compared to traditional algorithms, making it suitable for handling large-scale graphs efficiently.

In massively parallel processing (MPP) systems, an increase in data exchanges between processes often leads to higher time costs for these operations. Consequently, when the number of processes exceeds a certain threshold, the performance of a parallel program may degrade. This study examines the parallelization of an algorithm for finding the shortest path in a directed graph with cycles. The algorithm’s characteristics indicate that command-level parallelization yields limited efficiency. Meanwhile, the time complexity of the algorithm grows exponentially with increases in the number of vertices, edges, and cycles in the graph. Data-level parallelization is shown to be a viable approach. This paper presents a method to calculate the optimal number of processes for data-parallel algorithm optimization, based on the input data volume, to achieve maximum performance.

In the current digital era, Distributed Denial of Service (DDoS) attacks can be recognized as a prevalent and perilous network threat, posing substantial risks to both organizations and individuals. Consequently, the timely identification and notification of DDoS attacks play a vital role in their mitigation and reduction of ensuing harm. Long Short-Term Memory (LSTM) networks, a prevalent form of deep neural network, find extensive application in tasks like natural language processing and forecasting time series data. This study suggests the use of LSTM networks to train and categorize network data features, determining their association with cyberattacks. The effectiveness of our approach in detecting and signaling cyberattacks, especially DDoS attacks, is confirmed through experiments on real-world datasets.

This paper introduces a new shortest path algorithm based on the adjacency matrix representation of graphs. The algorithm searches for the shortest path between two certain vertices in the graph. The distinctive feature of the algorithm is that it works with loops and with negative weights. The algorithm divides the adjacency matrix into smaller submatrices for concurrent processing across multiple processors or threads. Experimental results on large graphs demonstrate significant speedup compared to sequential algorithms, with scalability analysis showcasing effective parallel resource utilization. The algorithm offers an efficient solution for computing shortest paths in large graphs, with potential applications in network routing and social network analysis.

This article focuses on evaluating the efficiency of machine learning classification algorithms in detecting malicious websites based on their URL addresses. A highly reliable dataset of URL addresses is used to train the machine learning classification model. The results show that the Random Forest algorithm achieves a high accuracy of 95.68% in detecting malicious websites and provides an effective solution to this problem. At the same time, software for detecting and warning of malicious website in two forms is also implemented: web applications and browser extensions.

The subject of this study is steganographic systems that are able to hide not only the content of the transmitted information but also the fact of its transmission. In this paper, we present a novel data transmission model within such systems, combining a modified Dijkstra’s algorithm with the dynamic utilization of multiple transmission routes. Consequently, this allows the simultaneous transmission of messages through parallel groups of routes with the dynamic alternation of these groups during the information exchange process between the sender and the receiver in the steganographic network. Thus, the speed, reliability, and security of message transmission are improved. Moreover, to increase the security level, the proposed model uses a route–time matrix that is available only to the sender and receiver. Additionally, the data transmission model has been tested on synthetic data and data obtained from real devices. The evaluation of the effectiveness of the proposed model on the example of various structured steganographic systems shows the significant effectiveness of our model. As a result, in comparison with existing similar methods, the secrecy index was increased five times and the violation index was decreased four times.

The problem of transferring data from a database of one type to a database of another type is relevant today due to the increase in data volumes and the complexity of tasks solved in various fields of human activity. The existing databases are less and less satisfying the needs of users. New types of databases appear that are more suitable for solving large-scale problems. This article proposes an algorithm for solving the problem of optimizing the document structure of a database based on metadata about the structure of a distributed relational database from which data are transferred. The approach also takes into account information about the structure of the priority database queries. The priority of database queries is user-defined. The system of automatic translation of the database, taking into account these metadata, allows the user to create a distributed document database that is optimal in two parameters: in terms of the volume of stored data and in terms of the execution time of priority database queries.

Most experts in the field of big data agree that the volume of data generated by various devices will increase exponentially in the future. Therefore, there is no doubt the relevance of solving the problem of data storage in such a way that access to the necessary information would be as quick as possible. In this article, we propose an approach based on set theory, which is suitable for databases that store information about objects with many relationships. Such databases can include both relational and NoSQL databases. This approach takes into account the relationships between objects and the structure of the most frequently executed database queries when designing the database architecture. This allows to remove operations “join” from queries and, at the same time, to minimize the data redundancy anomaly in a separate database structural unit. We demonstrated the application of this approach on three types of databases: relational, document, and columns family.

This article discusses an approach to the problem of translating a database from MySQL to Cassandra. In our approach, we use the graph theory to describe the overall database schema of both type databases: relational and NoSQL. The rules for selecting a column family schema based on a graph are described. A method for determining the structure of the Cassandra database, taking into account of the selected schemes of column families, is presented. At the end of the article, the results of testing the approach to translating a MySQL database into Cassandra format are presented, which showed its effectiveness and the possibility of applying it in practice.

This article discusses an approach to translate queries from MySQL to MongoDB taking into account the structure of the database which consists of four phases. In the first phase, the process of parsing the incoming queries MySQL are using building syntax grammar. In the second phase, a dictionary of query parts is created from the result of the first phase based on the aggregation structure MongoDB. In the next step, the structure of the database MongoDB is defined by converting collection data to structure tree and dictionary of query parts is updated for matching the structure of target database. In the last phase, the query of the database MongoDB is synthesized from building dictionary. In the end, this article shows the result of testing of the proposed approach with the different queries.

This article presents the results of the automatic process of building queries to the distributed document database based on SQL queries. Queries are submitted in the form of a graph. Next, taking into account the structure of the distributed database and information about sharding and replications, a graph is modified. Based on the database elements information to which queries are referred, sets are built. By operating on these sets, the optimal structure of document databases is determined, which is further optimized by the query graph. At the end of the article, the results of testing the proposed approach to the synthetic database generated by a special program are presented. Testing showed the correctness and efficiency of the application described in the approach article.

In the recent decades, NoSQL databases have become more popular day by day. And increasingly, developers and database administrators, for whatever reason, have to solve the problems of database migration from a relational model in the model NoSQL databases like the document-oriented database MongoDB database. This article discusses the approach to this migration data based on set theory. A new formal method of determining the optimal runtime searches aggregate collections with the attached documents NoSQL databases such as the key document. The attributes of the database objects are included in optimizing the number of collections and their structures in search queries. The initial data are object properties (attributes, relationships between attributes) on which information is stored in the database, and query the properties that are most often performed, or the speed of which should be maximal. This article discusses the basic types of connections (1-1, 1-M, M-M), typical of the relational model. The proposed method is the following step of the method of creating a collection without embedded documents. The article also provides a method for determining what methods should be used in the reasonable cases to make work with databases more effectively. At the end, this article shows the results of testing of the proposed method on databases with different initial schemes. Experimental results show that the proposed method helps reduce the execution time of queries can also significantly as well as reduce the amount of memory required to store the data in a new database.

In this article, we describe a new formalized method for constructing the NoSQL document database of MongoDB, taking into account the structure of queries planned for execution to the database. The method is based on set theory. The initial data are the properties of objects, information about which is stored in the database, and the set of queries that are most often executed or whose execution speed should be maximum. In order to determine the need to create embedded documents, our method uses the type of relationship between tables in a relational database. Our studies have shown that this method is in addition to the method of creating collections without embedded documents. In the article, we also describe a methodology for determining in which cases which methods should be used to make working with databases more efficient. It should be noted that this approach can be used for translating data from MySQL to MongoDB and for the consolidation of these databases.

В последние десятилетия все большую популярность набирают NoSQL базы данных, и все чаще разработчикам и администраторам таких баз по той или иной причине приходится решать задачу миграции баз данных из реляционной модели в модель NoSQL, например документно-ориентированную базу данных MongoDB. Описывается подход к такой миграции данных на основе теории множеств. Предлагаются правила для определения совокупности коллекций со вложенными документами NoSQL базы данных типа ключ-документ, оптимальной по времени выполнения поисковых запросов. Оптимизация числа коллекций и их структуры проводится с учетом атрибутов объектов базы данных, участвующих в поисковых запросах. Исходными данными являются свойства объектов (атрибуты, связи между атрибутами), информация о которых хранится в базе данных, и свойства запросов, которые наиболее часто выполняются или скорость их выполнения максимальна. В правилах учитываются основные типы связей (1-1, 1-М, М-М), свойственные реляционной модели. Рассматриваемая совокупность правил является дополнением к методу создания коллекций без вложенных документов. Также приводится методика для определения, в каких случаях какие методы надо использовать, чтобы сделать работу с базами данных более эффективной. В заключении приведены результаты тестирования предлагаемого метода на базах данных с различными начальными схемами. Результаты экспериментов показывают, что предлагаемый метод помимо сокращения времени выполнения запросов позволяет также значительно сократить объем памяти, необходимый для хранения данных в новой базе данных.

The work of transforming a database from one format periodically appears in different organizations for various reasons. Today, the mechanism for changing the format of relational databases is well developed. However, with the advent of new types of databases, such as NoSQL, this problem is prevalent due to the radically different ways of data organization at the various databases. This article discusses a formalized method based on set theory, at the choice of the number and composition of collections for a key-value type database. The initial data are the properties of objects, about which information is stored in the database, and the set of queries that are most frequently executed. The considered method can be applied not only when creating a new keyvalue database, but also when transforming an existing one, when moving from relational databases to NoSQL, when consolidating databases.