1 research outputs found
Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks
Existing distributed denial-of-service attack detection in software defined
networks (SDNs) typically perform detection in a single domain. In reality,
abnormal traffic usually affects multiple network domains. Thus, a cross-domain
attack detection has been proposed to improve detection performance. However,
when participating in detection, the domain of each SDN needs to provide a
large amount of real traffic data, from which private information may be
leaked. Existing multiparty privacy protection schemes often achieve privacy
guarantees by sacrificing accuracy or increasing the time cost. Achieving both
high accuracy and reasonable time consumption is a challenging task. In this
paper, we propose Predis, which is a privacypreserving cross-domain attack
detection scheme for SDNs. Predis combines perturbation encryption and data
encryption to protect privacy and employs a computationally simple and
efficient algorithm k-Nearest Neighbors (kNN) as its detection algorithm. We
also improve kNN to achieve better efficiency. Via theoretical analysis and
extensive simulations, we demonstrate that Predis is capable of achieving
efficient and accurate attack detection while securing sensitive information of
each domain