Fast-flux Botnet Detection Method Based on Spatiotemporal Feature of Network Traffic
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摘要:
僵尸网络已成为网络空间安全的主要威胁之一，虽然目前可通过逆向工程等技术来对其进行检测，但是使用了诸如fast-flux等隐蔽技术的僵尸网络可以绕过现有的安全检测并继续存活。现有的fast-flux僵尸网络检测方法主要分为主动和被动两种，前者会造成较大的网络负载，后者存在特征值提取繁琐的问题。因此为了有效检测fast-flux僵尸网络并解决传统检测方法中存在的问题，该文结合卷积神经网络和循环神经网络，提出了基于流量时空特征的fast-flux僵尸网络检测方法。结合CTU-13和ISOT公开数据集的实验结果表明，该文所提检测方法和其他方法相比，准确率提升至98.3%，召回率提升至96.7%，精确度提升至97.5%。
Abstract:Botnets have become one of the main threats to cyberspace security. Although they can be detected by techniques such as reverse engineering, botnets using covert technologies such as fast-flux can successfully bypass existing security detection and continue to survive. The existing fast-flux botnet detection methods are mainly divided into active and passive, the former will cause a large network load, and the latter has the problem of cumbersome feature value extraction. In order to effectively detect fast-flux botnets and alleviate the problems in traditional detection methods, a fast-flux botnet detection method based on spatiotemporal features of network traffic is proposed, combined with convolutional neural networks and recurrent neural network models, the fast-flux botnet is detected from both spatial and temporal dimensions. Experiments performed on the CTU-13 and ISOT public data sets show that compared with other methods, the accuracy rate of the proposed method is 98.3%, the recall rate is 96.7%, and the accuracy is 97.5%.
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Key words:
- Botnet /
- Fast-flux /
- Convolutional Neural Network (CNN) /
- Recurrent Neural Network (RNN)
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