基于深度神经网络的异常流量检测算法

Abnormal Traffic Detection Algorithm Based on Deep Neural Network

随着计算机网络和应用程序的规模呈指数级增长,攻击造成的潜在损害显著增加且越来越明显。传统异常流量检测方法已经不能满足当今互联网安全的需要,因此基于机器学习的算法成为针对复杂且不断增长的网络攻击的有效方法之一。文章提出基于深度神经网络的异常流量检测算法。通过对当前经典数据集进行对比,选择包含更多种攻击和协议类型的ISCX数据集进行实验分析。实验结果表明,与朴素贝叶斯算法对比,文章算法在提高准确率和降低误报率方面有了较大改善,是可用于异常流量检测的高效算法。

As the scale of computer networks and applications grows exponentially,the potential damage caused by attacks increases significantly and becomes more apparent. Traditional abnormal traffic detection methods can no longer meet the needs of Internet security,so machine learning-based algorithm has become one of the effective methods for complex and growing network attacks. This paper presents an abnormal traffic detection algorithm based on deep neural network. By comparing the current classical data sets,this paper chooses ISCX data set which contains more attack and protocol types for experimental analysis. The experimental results show that compared with naive Bayesian algorithm,the proposed algorithm greatly improves the accuracy and reduces the false alarm rate. It is an efficient algorithm for abnormal traffic detection.