Application of Big Data Analysis and Cloud Computing Technology

Abstract

Abstract With the swift growth of computer science, technologies such as big data and artificial intelligence are widely used in various fields of modern society. The types of network equipment and the scope of network coverage have also increased rapidly. While the network brings convenience to people, more attention must be paid to the security of the network platform. The purpose is to safely and effectively manage the current rapidly growing Internet data and improve the ability to detect abnormal network behaviors. Combining big data technology and machine learning (ML), the application of big data analysis and cloud computing technology for network security are studied. Firstly, the data collection technology of abnormal network behavior is introduced, and the Flume data collection component and Kafka distributed technology are discussed. Secondly, the data processing process and corresponding algorithm processing of abnormal network behavior are analyzed, including ML framework and stream processing technology. Finally, the model of network abnormal behavior detection based on big data is constructed, and compared with the related model based on the decision tree and random forest (RF) algorithm, and verified by experiments. The verification results reveal that among the 42 attack types against the dataset, the detection accuracy of network abnormal behavior by big data is 96.4%, and the false positive rate is 2.23%, which is higher than that of decision tree and RF algorithm. This experimental study denotes that the network abnormal behavior detection technology of big data based on the ML framework can effectively improve the type and efficiency of network abnormal behavior detection, and has certain reference significance for improving network security management and control capabilities.