Research on the application of a hybrid model based on deep learning and cluster analysis in anomaly detection of cloud platform access

With the rapid development of digitalization, cloud platforms have become a key technology supporting remote networking. However, the accompanying cybersecurity issues, particularly the detection of anomalous access activities, pose significant challenges in maintaining platform stability and user data security. This paper proposes a hybrid model that integrates a one-dimensional convolutional neural network (1D-CNN) with the DBSCAN clustering algorithm, aimed at enhancing the detection accuracy of anomalous access behaviors in cloud platforms. Through in-depth analysis and feature extraction of network traffic data, combined with the effective identification of abnormal patterns by the clustering algorithm, this study not only improves detection efficiency but also enhances the model's adaptability to novel anomalous behaviors. Experimental results demonstrate that this model surpasses traditional detection methods in multiple performance metrics such as accuracy, precision, recall, and F1 score, confirming its feasibility and effectiveness in practical application scenarios.