通信学报2018，Vol.39Issue(5)：11-22,12.DOI:10.11959/j.issn.1000-436x.2018073
基于ANN与KPCA的LDoS攻击检测方法
Detection method of LDoS attacks based on combination of ANN & KPCA
摘要
Abstract
Low-rate denial-of-service (LDoS) attack is a new type of attack mode for TCP protocol. Characteristics of low average rate and strong concealment make it difficult for detection by traditional DoS detecting methods. According to characteristics of LDoS attacks, a new LDoS queue future was proposed from the router queue, the kernel principal component analysis (KPCA) method was combined with neural network, and a new method was present to detect LDoS attacks. The method reduced the dimensionality of queue feature via KPCA algorithm and made the reduced dimension data as the inputs of neural network. For the good sell-learning ability, BP neural network could generate a great LDoS attack classifier and this classifier was used to detect the attack. Experiment results show that the proposed approach has the characteristics of effectiveness and low algorithm complexity, which helps the design of high performance router.关键词
低速率拒绝服务攻击/队列特征/核的主成分分析/神经网络Key words
low-rate denial of service/queue feature/kernel principal component analysis/neural network分类
信息技术与安全科学引用本文复制引用
吴志军,刘亮,岳猛..基于ANN与KPCA的LDoS攻击检测方法[J].通信学报,2018,39(5):11-22,12.基金项目
国家自然基金委员会与中国民航局联合基金资助项目(No.U1533107) （No.U1533107）
天津市自然科学基金资助项目(No.17JCZDJC30900)The Joint Foundation of National Natural Science Foundation and Civil Aviation Administration of China(No.U1533107),The Natural Science Foundation of Tianjin(No.17JCZDJC30900) （No.17JCZDJC30900）
