计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 178-182.doi: 10.11896/j.issn.1002-137X.2019.08.029
杜臻, 马立鹏, 孙国梓
DU Zhen, MA Li-peng, SUN Guo-zi
摘要: 对大量网络流量数据进行高质量特征提取与异常识别是做好网络取证的重要基础。文中重点研究并实现了网络取证中的数据处理并建立了模型库。对一种基于小波分析的网络流量异常检测方法进行了研究,用于检测包含两种不同注入攻击的pcap文件。文中的研究在Windows系统上进行,采用Python语言完成功能代码编写。首先从大量数据中提取需要的训练数据,然后使用小波分析提取特征,最后使用支持向量机进行分类器训练,从而可以利用该分类器识别出包含正常流量和异常流量的混合流量中的异常。定性和定量实验结果表明该方法对两种类型的异常流量实现了较高的分类精度,以期从特征提取和分类分析两个角度为网络取证的完善提供一种途径。
中图分类号:
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