Problem formulation and synthesis of statistical algorithms for recognising Web resources and their vulnerabilities by signatures of statistical and fuzzy linguistic features in cyberintelligence complexes

This study addresses the challenge of automating vulnerability recognition in web resources using statistical and fuzzy linguistic features. It presents a formalized approach for the fuzzy recognition of web resource vulnerabilities based on complex reference descriptions defined by signature intervals of statistical and fuzzy feature values. The research introduces algorithms for both single- and multi-alternative recognition of web resources, utilizing decision-making methods such as the minimax rule, Bayesian risk, maximum a posteriori probability, and maximum likelihood. The primary objective is to enhance the accuracy of vulnerability detection in web resources, especially under conditions of limited training data and fuzzy feature descriptions. The proposed algorithms aim to minimize decision errors and effectively classify vulnerabilities despite uncertain prior probabilities. This is particularly relevant in cybersecurity, where accurate threat detection and classification are critical. The research also highlights the practical value of these algorithms in improving the efficiency of cyber intelligence systems (CIs) for detecting security breaches and classifying web resource vulnerabilities. The proposed algorithms are designed to adapt to the complex and uncertain nature of web resource security, enabling better analysis of attack scenarios and the development of targeted protection strategies. In addition, the study identifies several challenges, including the complexity of formalizing reference descriptions for fuzzy features and the difficulties in applying traditional statistical recognition methods to web resources with fuzzy linguistic variables. The paper suggests future research directions, including developing new methodologies for processing large volumes of data and integrating these algorithms into modern cybersecurity systems. Overall, this research contributes to the field of cyber intelligence by offering novel solutions for automating the detection of web resource vulnerabilities, thus enhancing the security of online systems.