Formal modelling of software security requirements based on improved clustering algorithm and multi-modal information fusion

Manual analysis and verification are common means and methods of software security requirements that work at present, but they have the disadvantages of being long-consuming and low efficiency. In this paper, the k-means algorithm was used to distinguish feature points by k-value, calculate the probability of each point being selected as the cluster centre, and then obtain a new cluster number. Using the K-nearest neighbour (KNN) algorithm and spectral clustering principle, a clustering analysis method based on multi-attribute decision-making was constructed, which can better realise target recognition in a complex environment. The paper designed a contrast experiment based on the improved clustering algorithm. The results showed that the enhanced clustering algorithm can better model the software security requirements, this article include enhancing target recognition accuracy in complex environments using the k-means algorithm with variable k-values for clustering, integrating the KNN algorithm with spectral clustering principles for effective identification in complex environments.