Abstract
As cyber threats become more complex, traditional vulnerability detection methods lose their effectiveness. The purpose of this work is to develop and test an approach to identifying vulnerabilities based on the analysis of data from thematic Internet resources: forums, blogs and social networks. These sources contain a large amount of unstructured information, which requires the use of data mining methods. The work uses the integration of modern technologies: the pre-trained SecBERT language model (Security Bidirectional Encoder Representations from Transformers), designed for cybersecurity tasks, and the adaptive neuro-fuzzy inference system DENFIS (Dynamic Evolving Neural-Fuzzy Inference System). The proposed system allows you to filter irrelevant messages, highlight indicators of compromise and potential threats. The use of fuzzy logic makes it possible to efficiently process vague and incomplete information. Experiments confirmed high classification accuracy and stable fuzzy clustering performance (FPC = 0.93; PE = 0.28; XB = 0.042). The system demonstrated the ability to promptly detect signs of cyber threats and has scalability potential for monitoring and attack prediction tasks. The results indicate its potential in increasing the speed of response to cyber threats and strengthening the protection of information systems.