计算机科学
服务拒绝攻击
加密
领域(数学分析)
计算机网络
私人信息检索
信息隐私
计算机安全
数据挖掘
互联网
数学
数学分析
万维网
作者
Liehuang Zhu,Xiangyun Tang,Meng Shen,Xiaojiang Du,Mohsen Guizani
标识
DOI:10.1109/jsac.2018.2815442
摘要
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 privacy-preserving 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI