Security vulnerabilities in web traffic can directly lead to data leak. Preventing these data leaks to a large extent has become an important problem to solve. Besides, the accurate detection and prevention of abnormal changes in web traffic is of great importance. In this study, a hybrid approach, called C-NSA, based on the negative selection algorithm (NSA) and clonal selection algorithm (CSA) of artificial immune systems for the detection of abnormal web traffic on the network is proposed and a user-friendly application software is developed. The real and synthetic data in the Yahoo Webscope S5 dataset are used for web traffic and the data are split into windows using the window sliding. In the experimental studies, the abnormal web traffic data is detected by monitoring the changes in the number of activated detectors in the C-NSA. It is observed that the average accuracy performance of finding anomalies in real web traffic data is 94.30% and the overall classification accuracy is 98.22% based on proposed approach. In addition, false positive rate of the proposed approach using C-NSA is obtained as 0.029. In addition, the results in synthetic web traffic data using C-NSA are achieved as average 98.57% classification accuracy.

中文翻译：

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C-NSA：一种基于人工免疫算法的混合方法，用于Web流量异常检测

Web流量中的安全漏洞可能直接导致数据泄漏。很大程度上防止这些数据泄漏已成为要解决的重要问题。此外，准确检测和防止Web流量异常变化也非常重要。在这项研究中，提出了一种基于人工免疫系统的阴性选择算法（NSA）和克隆选择算法（CSA）的混合方法，称为C-NSA，用于检测网络上的异常Web流量，并且用户友好开发应用软件。Yahoo Webscope S5数据集中的真实和合成数据用于网络流量，并且使用窗口滑动将数据拆分为多个窗口。在实验研究中，通过监视C-NSA中激活的检测器数量的变化来检测异常的Web流量数据。可以发现，基于提出的方法，在真实的网络流量数据中发现异常的平均准确度性能为94.30％，整体分类准确度为98.22％。此外，使用C-NSA提出的方法的误报率为0.029。此外，使用C-NSA获得的综合网络流量数据的结果平均分类精度为98.57％。