A Novel Visualization Approach for Efficient Network Scans Detection

Abstract

Network scans visualization provides very effective means for to detection large scale network scans. Many visualization methods have been developed to monitor network traffic, but all the techniques or tools still heavily rely on human detection. They seldom consider the importance of network event characteristics to the network data visualization, and cannot detect slow scans, hidden scans etc. In this paper a visual interactive network scans detection system called ScanViewer is designed to represent traffic activities that reside in network flows and their patterns. The ScanViewer combines the characteristics of network scan with novel visual structures, and utilizes a set of different visual concepts to map the collected datagram to the graphs that emphasize their patterns. Additionally, a new tool named localport is designed for to capture large-scale ports information. The experiments show that ScanViewer can not only detect network scans, port scans, distributed port scans, but also can detect the hidden scans etc.