【摘要】 针对现有网络异常流量检测算法鲜少关注网络流量这类时间序列数据在时间上的依赖关系以及没有从时间周期角度对网络异常流量进行检测的问题，提出一种基于CGAN-LSTM的无监督网络异常流量检测算法。首先使用LSTM结构的生成器和判别器学习正常样本的数据分布，其次使用时间周期信息指导生成器G生成样本，最后同时使用生成器的重构误差和判别器的判别结果判别测试样本。实验结果显示，该算法在ISCX2012数据集和CICIDS2017数据集上的F1值分别达到89.38%、85.62%，与现有无监督异常流量检测算法相比具有更好的检测性能。更多还原

【Abstract】 Aiming at the problem that the current network abnormal traffic detection methods pay little attention to the time dependence of network traffic and do not detect network abnormal traffic from the perspective of time cycle,this paper proposes an unsupervised network abnormal traffic detection method based on CGAN-LSTM. This method uses the generator and discriminator of LSTM structure to learn the data characteristics of normal samples,uses time period information to guide generator to generate samples,and finally uses the reconstruction error of generator and the discrimination result of discriminator to discriminate test samples at the same time. The experimental results show that the F1scores of this algorithm on ISCX2012 data set and CICIDS2017 data set reach 89.38% and85.62% respectively,which has better detection performance compared with the existing unsupervised abnormal traffic detection algorithms.更多还原