四川大学学报（自然科学版）2017，Vol.54Issue(1)：71-75,5.DOI:103969/j.issn.0490-6756.2017.01.012
基于BP神经网络的DDoS攻击检测研究
Research on DDoS detection based on BP neural network
摘要
Abstract
Distributed denial of service attack (DDoS) is a common threat in today's networks.While such an attack is not difficult to launch,defending a network resource against it is disproportionately difficult.This paper analysis some famous theories and methods on detection of DDoS network attacks systematically based on the fast neural network algorithm.Meanwhile,the attack traffic feature model which is constructed based on the packet length,packet transmission time interval and packet length change rate etc is proposed.Second,a method to optimize the parameters of the neural network error is also proposed by a large number of attempts.Finally,the UCLA dataset is used to carry out the contrast experiment of the parameters before and after the improvement.Experiments show that the proposed method can effectively detect DDoS attacks and has a better generalization ability.关键词
BP神经网络/DDoS攻击/入侵检测Key words
BP neural network/DDoS attack/Intrusion detection分类
信息技术与安全科学引用本文复制引用
杨可心,桑永胜..基于BP神经网络的DDoS攻击检测研究[J].四川大学学报（自然科学版）,2017,54(1):71-75,5.基金项目
四川省应用基础研究计划项目(2013JY0018) （2013JY0018）
