@inproceedings{813e1bd9b1dc4cd1a0b93d9d976b1d9e,
title = "Privacy-preserving anomaly detection across multi-domain for software defined networks",
abstract = "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.",
keywords = "Anomaly detection, Multi-domain collaboration, Privacy-preserving, Software defined network",
author = "Huishan Bian and Liehuang Zhu and Meng Shen and Mingzhong Wang and Chang Xu and Qiongyu Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 7th International Conference on the Theory, Technologies and Applications of Trusted Systems, INTRUST 2015 ; Conference date: 07-12-2015 Through 08-12-2015",
year = "2016",
doi = "10.1007/978-3-319-31550-8_1",
language = "English",
isbn = "9783319315492",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--16",
editor = "Moti Yung and Jianbiao Zhang and Zhen Yang",
booktitle = "Trusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers",
address = "Germany",
}