Security Situation Assessment Model of Big Data Network Based on K-means Clustering Algorithm

At present, network information acquisition, communication, business transactions and other application fields are used more and more frequently. The ensuing security problems are also emerging one after another. Internet safty has become an important factor affecting social stability, but there are still some drawbacks in the previous internet safty situation assessment technology. In order to solve the existing problems, this article studies the processing and prediction methods of data sources in internet safty situation awareness, and selects multiple data sources as sensing elements to process and predict respectively and strengthen protection; Then the security situation assessment model of big data network based on improved $K -$means clustering algorithm is proposed and constructed. Using this model to assess and predict the internet safty situation, the potential threats can be assessd according to the current attack scenarios, and the real-time network attacks have obvious situation changes. The simulation results show that the evaluation accuracy of this internet safty situation assessment model can reach more than 94%, which is about 8.54% higher than that of the traditional $K -$means method, and the algorithm has high real-time performance. This model provides a new idea for internet safty situation assessment in the field of big data.