基于深度学习和三支决策的DDoS攻击检测算法

A DDoS Attack Detection Algorithm Based on Deep Learning and Three-way Decisions

针对软件定义网络(Software Defined Ntwork,SDN)中的分布式拒绝服务(Distribute Denial of Service,DDoS)攻击检测的方法少、现存方法入侵检测率低的问题,提出了一种基于深度学习和三支决策的入侵检测算法。首先使用深度信念网络对SDN的流表项进行特征提取,然后利用基于三支决策理论的入侵检测模型进行DDoS攻击的入侵检测,对于正域和负域的数据直接进行分类,对于边界域中的数据使用K近邻算法重新进行分类。仿真实验结果表明,与其他入侵检测模型相比,所提算法的入侵检测效率更高。

Aiming at the problem of few DDoS attack detection methods and low intrusion detection rate of existing methods in software defined network(SDN),an intrusion detection algorithm based on deep learning and three decision making is proposed.First,use deep belief network to extract features of SDN flow entries,then use three-way decisions intrusion detection model forintrusion detection of DDoS attacks,directly classify data in the positive and negative domains,and the data in the boundary domain is reclassified using the K-nearest neighbor algorithm.Simulation results show that compared with other intrusion detection models,the detection rate of this methodis higher,and the false alarm rate is lower.