INDAGO: A New Framework For Detecting Malicious SDN Applications

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 applicati...

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Bibliographic Details
Published in2018 IEEE 26th International Conference on Network Protocols (ICNP) pp. 220 - 230
Main Authors Lee, Chanhee, Yoon, Changhoon, Shin, Seungwon, Cha, Sang Kil
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2018
Subjects
Data mining
Data structures
Feature extraction
Malware
malware detection
Routing
SDN
SDN security
Security
Semantics
static analysis
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Summary: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.
DOI:10.1109/ICNP.2018.00031