You will compare situations or scenarios more easily.You can digest larger amounts of data more easily.You have a greater ability to recognize trends & patterns.When you apply visualization methods to data analysis, you are more likely to cut the time spent looking for information because: As we previously wrote, graph visualization is critical for the analysis of graph dat. There is a reason researchers started to develop these tools. Even though these applications have long been confined to the field of research, it was the birth of computer tools for graph visualization.

In the field of graph theory and network science, researchers started to imagine graph analysis and visualization tools as early as 1996 with the Pajek project. Graph visualization tools turn connected data into graphical network representations that takes advantage of the human brain proficiency to recognize visual patterns and more pattern variations. Representing connected data in tables is not intuitive and often hides the connections in which lies the value. While it’s easy to read and comprehend non-graph data in a tabular format such as a spreadsheet, you will probably miss valuable information if you try to analyze connected data the same way. It helps surface information and insights leading to the understanding of a situation, or the solving of a problem. Visualization tools represent an important bridge between graph data and analysts.

For decades, visual representations have helped researchers, analysts and enterprises derive insights from their data. The visualization of information has been the support of many types of analysis, including Social Network Analysis.

In case studies, we describe the forensic process of this system, including identifying port scan, distributed denial of service, and botnet attacks, on the datasets in VAST Challenge 2013.The third layer of graph technology that we discuss in this article is the front-end layer, the graph visualization one. The system provides a multilevel analysis architecture and multifaceted perspectives for comprehensive cognition in traffic forensics. The system consists of three collaborative views, including the Timeline view integrating active features and individual dispersions based on information entropies for the perception of the overall time series, the Matrix view balancing the expression of network structure and distributions of IPs and ports for efficient events tracing, and the Historical view comparing statuses in successive time slots for dynamic trends tracking. In this paper, we present a matrix-based visualization system for visualized forensic analysis on unintelligible traffic datasets. Visualization techniques can convert abstract data into visual sensitive graphics thus, forensic officers can extract useful information quickly. Network forensics requires analysts to efficiently reason about various attack phenomena from massive data.

Data visualization network nodebox