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Privacy-Preserving Anomaly Detection Across Multi-domain for Software Defined Networks
Book chapter   Peer reviewed

Privacy-Preserving Anomaly Detection Across Multi-domain for Software Defined Networks

Huishan Bian, Liehuang Zhu, Meng Shen, Mingzhong Wang, Chang Xu and Qiongyu Zhang
Trusted Systems, pp.3-16
International Conference on Trusted Systems (INTRUST), 7th (Beijing, China, 07-Dec-2015–08-Dec-2015)
Lecture Notes in Computer Science (LNCS), 9565, Springer International Publishing
2016
DOI: https://doi.org/10.1007/978-3-319-31550-8_1
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https://doi.org/10.1007/978-3-319-31550-8_1View
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Abstract

privacy-preserving multi-domain collaboration anomaly detection software defined network
Software Defined Network (SDN) separates control plane from data plane and provides programmability which adds rich function for anomaly detection. In this case, every organization can manage their own network and detect anomalous traffic data using SDN architecture. Moreover, detection of malicious traffic, such as DDoS attack, would be dealt with much higher accuracy if these organizations shared their data. Unfortunately, they are unwilling to do so due to privacy consideration. To address this contradiction, we propose an efficient and privacy-preserving collaborative anomaly detection scheme. We extend prior work on SDN-based anomaly detection method to guarantee accuracy and privacy at the same time. The implementation of our design on simulated data shows that it performs well for network-wide anomaly detection with little overhead.

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