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A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

机译：基于决策树和自适应多层神经网络的P2P僵尸网络检测方案

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摘要

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.
机译：近年来，僵尸网络已被用作在Internet上承载和传播许多恶意代码的流行方法。这些恶意代码为执行许多欺诈活动（包括垃圾邮件，分布式拒绝服务攻击和点击欺诈）铺平了道路。虽然许多僵尸网络使用集中式通信体系结构进行设置，但对等（P2P）僵尸网络可以采用分散网络结构，该体系结构使用覆盖网络来交换命令和控制数据，从而使检测更加困难。这项工作提出了一种基于自适应多层前馈神经网络并结合决策树的P2P Bot检测方法。分类和回归树被用作特征选择技术以选择相关特征。利用这些功能，使用弹性反向传播学习算法创建了多层前馈神经网络训练模型。对基于决策树的特征集选择，主成分分析和ReliefF算法的比较表明，具有基于决策树的特征选择的神经网络模型具有较高的识别准确率和较低的误报率。通过对实际网络流量数据集进行实验，证明了该方法的有效性。在这些实验中，观察到的平均检出率为99.08％，假阳性率为0.75％。

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