Dr Sky Miao

Visualization is an important tool for capturing the network activities. Effective visualization allows people to gain insights into the data information and discovery of communication patterns of network flows. Such information may be difficult for human to perceive its relationships due to its numeric nature such as time, packet size, inter-packet time, and many other statistical features. Many existing work fail to provide an effective visualization method for big network traffic data. This work proposes a novel and effective method for visualizing network traffic data with statistical features of high dimensions. We combine Principal Component Analysis (PCA) and Mutidimensional Scaling (MDS) to effectively reduce dimensionality and use colormap for enhance visual quality for human beings. We obtain high quality images on a real-world network traffic dataset named ¿ISP¿. Comparing with the popular t-SNE method, our visualization method is more flexible and scalable for plotting network traffic data which may require to preserve multi-dimensional information and relationship. Our plots also demonstrate the capability of handling a large amount of data. Using our method, the readers will be able to visualize their network traffic data as an alternative method of t-SNE.

Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing untrained intrusion detection systems (IDSs). Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear identification of ¿normal¿ clusters and described distinct clusters of effective attacks.