Application of Big Data Analysis and Cloud Computing Technology

Abstract

While networks bring convenience to people, more attention must be paid to the security of the network platform. This study combines big-data technology and machine learning (ML) to investigate the application of big-data analysis and cloud-computing technology in network security. First, the data-collection technology of abnormal network behavior is introduced, and the Flume data-collection component and Kafka distributed technology are discussed. Second, the data-processing process of abnormal network behavior and the corresponding algorithm processing are analyzed. Finally, an abnormal network behavior detection model based on big data is constructed and compared with related models based on decision-tree and random-forest (RF) algorithms. The experimental results show that the big-data anomalous network behavior detection technology based on the ML framework can effectively improve the type and efficiency of anomalous network behavior detection, which is of some significance for improving the network security control capability.