This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.

In the era of rapid digital technology development, the volume of information transmitted incyberspace is significantly increasing, thereby intensifying the need to ensure confidentiality and the protectionof personal data. Modern cryptographic methods are used for such purposes. One of the most widely usedencryption algorithms is Advanced Encryption Standard (AES), which employs substitution box (S-box) toenhance the security of the encryption process. S-box is one of the important components of the block algorithm.The robustness of an S-box is largely determined by its architecture and algebraic basis. In this paper, a methodfor constructing S-boxes is proposed based on various alternative irreducible polynomials with the addition of aconstant 8-bit vector, different from that used in the AES standard. A total of 16 S-boxes were constructed andtheir performance was evaluated using standard cryptographic criteria, including nonlinearity, strict avalancheeffect, etc. The results demonstrated that the proposed S-boxes exhibit performance comparable to that of thestandard AES S-box. This study is conducted within the framework of grant funding from the Science Committeeof the Ministry of Education and Science of the Republic of Kazakhstan (grant No. AP19677422).

This article discusses the software implementation of an encryption algorithm based on the Glushkov product of finite automata. The main focus of this work is on the application of this mathematical model in cryptography, which allows formalizing the process of key generation and construction of block ciphers. The paper provides a theoretical overview of finite automata without output, their properties and features, as well as a formal definition of the Glushkov product. The encryption algorithm is described, its stages are detailed, including the construction of a key automaton and the process of encryption and decryption of data. To confirm the effectiveness of the proposed method, its software implementation in Python was carried out. Experimental results demonstrate the practical applicability of the algorithm, its cryptographic resistance and potential directions for further development. The study shows that the Glushkov product can serve as a basis for the development of new cryptographic schemes with a high degree of protection.

In the context of rapid digital technology development, information security has become one of the key factors for the sustainable functioning of both society and the state. Modern methods of information protection rely on cryptographic algorithms, whose effectiveness is determined not only by their mathematical rigor but also by the degree of innovation in the applied models. One of the promising directions in this field is the use of finite automata models, which possess significant theoretical potential and broad opportunities for practical implementation. This study is devoted to the description of algorithms for generating invertible linear and nonlinear finite automata with input-output memory, applied in the FAPKC cryptosystem series. Unlike previously published works that are limited to purely mathematical descriptions of such models, this paper presents concrete procedures for their construction and implementation. Furthermore, a set of statistical tests was conducted to evaluate the randomness and cryptographic strength of the generated automata. The experimental results confirm their cryptographic robustness and demonstrate the feasibility of applying the proposed algorithms in modern cryptographic protocols.