Application-level network traffic analysis and sophisticated analysis techniques such as machine learning and stream data processing for network traffic require considerable computational resources. In addition, developing an application protocol analyzer is a tedious and time-consuming task. Therefore, we propose a scalable and flexible traffic analysis platform (SF-TAP) that provides an efficient and flexible application-level stream analysis of high-bandwidth network traffic. Our platform's flexibility and modularity allow developers to easily implement multicore scalable application-level stream analyzers. Furthermore, SF-TAP is horizontally scalable and can therefore manage high-bandwidth network traffic. We achieve this scalability by separating network traffic based on traffic flows, forwarding the separated flows to multiple SF-TAP cells, each of which consists of a traffic capturer and application-level analyzers. In this study, we discuss the design and implementation of SF-TAP and provide details of its evaluation.
['Yuuki Takano', 'Ryosuke Miura', 'Shingo Yasuda', 'Kunio Akashi', 'Tomoya Inoue']
SF-TAP: scalable and flexible traffic analysis platform running on commodity hardware