The ability to compare and contrast different sets of data visually has a lot of potential as a way to bring people together. It is important to have access to different ways of showing the same or similar information if you want to analyse data at different levels. Approaches that use network-based visualisation to find intrusions use graphs to show information like the source and destination addresses, as well as the port numbers and packets themselves. Graph-based methods of detection can be used to show that someone has broken into a network. This type of figure shows how the formation and growth of networks are fluid and always changing. Even though analysing anomalies in large-scale networks is very important, it might be hard to do because the dynamics are not linear and the graph gets more complicated as the size of the network grows. Using a lot of different kinds of complicated data is another problem that needs to be solved. Dealing with the many different types of data and file formats that come up when working with Big Data can be hard and take a lot of time. Using high-performance computing (HPC) and, more specifically, graphics processing units (GPUs) for Big Data analytics is a great way to speed up scientific computing, network analysis, and network visualisation. This is because GPUs are much better at handling graphics than CPUs. Future research may focus on Big Data analytics for streaming data, Big Data with complex structures, or Big Data with uncertainty.

Big Data analytics , Visualization In Networking , data integration .