Visual analysis framework for network abnormal data based on multi-agent model

Abstract

With the development of Internet, the amount of data increases exponentially. With the development of network technology, human beings have entered the era of big data. Therefore, it is very important to find valuable information from massive network data. At present, most of the network security products in the market record the system test results in the form of logs. Users can view and analyze the log information one by one to find out the suspicious behavior and carry out the next diagnosis. Finally, they can defend the identified attacks. Intrusion detection system usually stores detection results in the form of alert files. There may be a large number of redundant or false alarm information in the alarm file, so the heavy cognitive burden of users has become one of the disadvantages of traditional intrusion detection system. In consideration of the above situation, visualization technology has been introduced into the field of network security by researchers. The network visualization analysis displays the massive network data and log information in the way of graph and image, and uses the developed human vision to process the massive graphical data. Data visualization is a method of transforming abstract symbols into concrete geometric figures, which presents the results of simulation and calculation in the form of figures and images. In this paper, multi-agent model is used to build a network data visualization analysis model. The experimental results show that the method in this paper can effectively visualize a large number of network data, and the experimental results are easy to understand.