Adaptive Incremental Modeling Combined with Hidden Markov Modeling in Cyber Security

This study examines the limitations of traditional CS technology, which relies heavily on labeled data and is unable to detect new types of attacks in real time. It proposes an optimization and improvement of CS technology through the use of hidden Markov models and adaptive incremental models. The research is conducted from three perspectives: the actual collection of security information, the extraction of unknown protocol features, and the development of detection models. Firstly, a unified method of collecting safety information is established, and a safety information database is obtained by combining information filtering, integration, and association analysis. Secondly, the modified hidden Markov model is used to parse the unknown protocol messages and extract the appropriate features. Finally, the extracted information features are applied to the adaptive incremental model for intrusion detection. The experimental results indicated that the average time cost of the data processing method is 25.841 ms, and the identification accuracy of the intrusion detection model for new attack types reaches 91.15%. The model designed by the research can adapt to the complex and changeable network environment and accurately detect network intrusion while ensuring operational efficiency, which provides a new research direction for the field of CS.