2018 IEEE 26th International Conference on Network Protocols (ICNP) (2018)
Cambridge, United Kingdom
Sep 25, 2018 to Sep 27, 2018
Software-Defined Networking (SDN) controllers not only provide centralized control of SDNs, but also implement open and programmable APIs to ultimately establish an open network environment, where anyone can develop and deliver useful SDN applications. In such an environment, malicious SDN applications can be easily developed and distributed by untrusted entities and can even possess full control of SDNs. Thus, the security threat of malicious SDN applications must be taken seriously. In this paper, we propose a novel system, called Indago, which statically analyzes SDN applications to model their behavioral profiles, and finally, it automatically detects malicious SDN applications with a machine learning approach. We implement a prototype system and evaluate its effectiveness with real world SDN applications and malware. Our evaluation results show that the system can detect most known SDN malware with a high detection rate and low error rates.
application program interfaces, computer network management, invasive software, learning (artificial intelligence), software defined networking
C. Lee, C. Yoon, S. Shin and S. K. Cha, "INDAGO: A New Framework For Detecting Malicious SDN Applications," 2018 IEEE 26th International Conference on Network Protocols (ICNP), Cambridge, United Kingdom, 2018, pp. 220-230.