Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network
Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network
复制标题
基于小波能谱和组合神经网络的LDoS攻击流量识别
DOI：
10.1002/dac.3449
复制
发表时间：
2018-01-25
影响因子：
2.1
通讯作者：
Wang, Minxiao
中科院分区：
文献类型：
--
作者：
Yue, Meng;Liu, Liang;Wang, Minxiao
作为一种特殊类型的拒绝服务(DoS)攻击，以TCP为目标的低速率拒绝服务(LDOS)攻击具有平均速率低、隐蔽性强的特点，因此很难识别此类攻击流量。针对网络流量具有多重分形特征的特点，提出了一种基于小波变换和组合神经网络的网络流量识别方法，对正常网络流量和LDOS攻击流量进行分类。利用从采样流量中提取的小波能量谱系数对不同时间尺度上的流量进行多重分形分析。对于表现出不同多重分形特征的多尺度谱系数，设计了组合神经网络对正常网络流量和LDOS攻击流量进行分类。试验台实验结果表明，该方法能够准确识别LDOS攻击流量。
As a special type of denial of service (DoS) attacks, the TCP-targeted low-rate denial of service (LDoS) attacks have the characteristics of low average rate and strong concealment, so it is difficult to identify such attack traffic. As multifractal characteristics exist in network traffic, a new identification approach based on wavelet transform and combined neural network is proposed to classify normal network traffic and LDoS attack traffic. Wavelet energy spectrum coefficients extracted from the sampled traffic are used for multifractal analysis of traffic over different time scale. The combined neural network is designed to classify these multiscale spectrum coefficients that show different multifractal characteristics belonging to normal network traffic and LDoS attack traffic. Test results of test-bed experiments indicate that the proposed approach can identify LDoS attack traffic accurately.