通信学报2017，Vol.38Issue(5)：19-30,12.DOI:10.11959/j.issn.1000-436x.2017075
基于联合特征的LDoS攻击检测方法
Approach of detecting low-rate DoS attack based on combined features
摘要
Abstract
LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68％.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.关键词
低速率拒绝服务攻击/联合特征/BP神经网络/异常检测Key words
low-rate denial of service attack/united features/BP neural network/anomaly detection分类
信息技术与安全科学引用本文复制引用
吴志军,张景安,岳猛,张才峰..基于联合特征的LDoS攻击检测方法[J].通信学报,2017,38(5):19-30,12.基金项目
国家自然科学基金资助项目(No.U1533107,No.U1433105) （No.U1533107,No.U1433105）
中央高校基本科研业务基金资助项目(No.3122016D003) （No.3122016D003）
中国民航大学研究生课程案例开发基金资助项目 （）
天津市自然科学重点基金资助项目(No.17JCZDJC30900).The National Natural Science Foundation of China (No.U1533107,No.U1433105),Fundamental Scientific Research Foundation of the Central University (No.3122016D003),Case Development Project of Graduate Program in Civil Aviation University of China,Key Project of Tianjin Natural Science Foundation (No.17JCZDJC30900) （No.17JCZDJC30900）
