一种基于 RNN 区分 DDoS 攻击类型的方法

A Method to Distinguish DDoS Attack Types Based on RNN

随着网络技术的广泛应用,出现了多种多样的网络攻击,其中,分布式拒绝服务(Distributed Denial of Service,DDoS)攻击的危害性较大。将12种DDoS攻击的数据与正常数据流混在一起后难以区分,因此防御DDoS攻击的关键是对其进行有效区分。文章提出一种基于RNN区分DDoS攻击类型的方法。以循环神经网络(Recurrent Neural Network,RNN)为研究改进对象,运用了模型的模块化研究方法和技术,抽象出3类简单模块组合形成RNN-IDDoS模型,该模型具有5层、3种时间步。在公开数据集上进行实验,实验结果表明该模型的准确率可达99.8%,优于当前其他3种模型,取得了很好的区分效果。

With the wide application of network technology,there are a variety of network attacks,among which distributed denial of service(DDoS)attacks are more harmful.The 12 types of DDoS attacks are mixed with normal data flows and are difficult to distinguish.The primary problem of defending against DDoS attacks is to distinguish them effectively.For the first time,this paper aimed to distinguish for research purposes attack types.It is proposed a method to distinguish DDoS attack types based on Recurrent neural network(RNN).RNN is a research object,with the modularization research methods and techniques, three types of simple modules are abstracted, and combined to form the RNN- IDDoS model. This model has five layers, three-time steps. Experiments on public datasets show that the proposed model can achieve an accuracy of 99.8%, which is better than the experimental test conclusions of the other three current models and has achieved good discrimination results.