This study takes a look into the application of the Naive Bayes machine learning algorithm to enhance the accuracy of Intrusion Detection Systems (IDS). The primary focus is to assess the algorithm's performance in detecting various types of network attacks, particularly Denial of Service (DoS) attacks. This research proposes using Naive Bayes to improve intrusion detection systems that struggle to keep pace with evolving cyber threats. This study evaluated the efficiency scores of the Naive Bayes classifying model for two different dependency scenarios and identified strong and weak properties of this model. The Naive Bayes classifier demonstrated satisfactory results in detecting network intrusions, especially in binary classification scenarios where the goal is to distinguish normative and malicious traffic due to its simplicity and efficiency. However, its performance declined in multi-class classification tasks, where multiple types of attacks need to be differentiated. The study also highlighted the importance of data quality and quantity in training machine learning models because of the impact of those parameters on the model efficiency. The USB-IDS-1 dataset, while useful, has limitations in terms of the variety of attacks. Using datasets with a wider range of attack types could significantly improve the accuracy of IDS. The findings of this research can be applied to such domains as network security, cybersecurity, and data science. The Naive Bayes classifier can be integrated into IDS systems to enhance their ability to detect and respond to cyber threats. However, it is essential to consider the limitations of the algorithm and the specific conditions of its environment. To maximize the effectiveness of the Naive Bayes classifier, it could be promising to optimize and normalize the data to improve the accuracy of the model and combine Naive Bayes with the other machine learning algorithms to address its limitations

The current evolution of disseminated computer program frameworks is characterized by a growing adherence to the principles of service-oriented engineering (SOA). Simultaneously, these frameworks are becoming more intricate, with an increasing number of components and more complex data connections between them. This situation underscores the importance of employing mechanisms to unify artifacts in the process of developing composite web services, which govern, among other aspects, the architectural plane of the frameworks under construction. A model for constructing composite web services is suggested as a suitable tool, implemented in accordance with a hierarchical approach, intended for use in designing distributed systems. Model is constructed over an assumption that coordination of the components of a composite web service is carried out in a centralized manner. in accordance with the orchestration model. To implement formalization and obtain, based on analytical representations, the corresponding software implementations, it has been decided to use the DEVS mathematical apparatus. The aspect of software implementation is considered pivotal in determining the feasibility of automating the acquisition of composite web services that operate within the orchestration model. Obtained research results has been interpreted as a conrmation of the eectiveness of this approach on the basis of the scenario of querying the database. Resulting artifacts have been represented with UML notation. The relationship between analytical representations and corresponding software implementations has also been demonstrated. Usage of the DEVS Suite tools has made it possible to visualize the process of simulation - to obtain estimated values of the indexes of the resulting solutions

This paper presents a novel numerical method for solving the control system synthesis problem through the application of machine learning techniques, with a particular focus on symbolic regression. Symbolic regression is used to automate the development of control systems by constructing mathematical expressions that describe control functions based on system data. Unlike traditional methods, which often require manual programming and tuning, this approach leverages machine learning to discover optimal control solutions. The paper introduces a general framework for machine learning in control system design, with an emphasis on the use of evolutionary algorithms to optimize the generated control functions. The key contribution of this research lies in the development of an algorithm based on the principle of small variations in the baseline solution. This approach significantly enhances the efficiency of discovering optimal control functions by systematically exploring the solution space with minimal adjustments. The method allows for the automatic generation of control laws, reducing the need for manual coding, which is especially beneficial in the context of complex control systems, such as robotics. To demonstrate the applicability of the method, the research applies symbolic regression to the control synthesis of a mobile robot. The results of this case study show that symbolic regression can effectively automate the process of generating control functions, significantly reducing development time while improving accuracy. However, the paper also acknowledges certain limitations, including the computational demands required for symbolic regression and the challenges associated with real-time implementation in highly dynamic environments. These issues represent important areas for future research, where further optimization and hybrid approaches may enhance the method's practicality and scalability in real-world applications.