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.
['Huishan Bian', 'Liehuang Zhu', 'Meng Shen', 'Mingzhong Wang', 'Chang Xu', 'Qiongyu Zhang']
Privacy-Preserving Anomaly Detection Across Multi-domain for Software Defined Networks