Internet of Things (IoT) devices in Smart Home Network (SHN) are highly vulnerable to complex botnet attacks. In this paper, we investigate the effectiveness of Recurrent Neural Network (RNN) to correctly classify network traffic samples in the minority classes of highly imbalanced network traffic data. Multiple layers of RNN are stacked to learn the hierarchical representations of highly imbalanced network traffic data with different levels of abstraction. We evaluate the performance of Stacked RNN (SRNN) model with Bot-IoT dataset. Results show that SRNN outperformed RNN in all classification scenarios. Specifically, SRNN model learned the discriminating features of highly imbalanced network traffic samples in the training set with better representations than RNN model. Also, SRNN model is more robust and it demonstrated better capability to effectively handle over-fitting problem than RNN model. Furthermore, SRNN model achieved better generalization ability in detecting network traffic samples of the minority classes.

智能家居网络（SHN）中的物联网（IoT）设备非常容易受到复杂的僵尸网络攻击。在本文中，我们研究了递归神经网络（RNN）在高度不平衡的网络流量数据的少数类别中正确分类网络流量样本的有效性。RNN的多层被堆叠以学习具有不同抽象级别的高度不平衡的网络流量数据的分层表示。我们使用Bot-IoT数据集评估Stacked RNN（SRNN）模型的性能。结果表明，在所有分类方案中，SRNN均优于RNN。具体而言，SRNN模型在训练集中学习了高度不平衡的网络流量样本的区别特征，并且具有比RNN模型更好的表示形式。还，SRNN模型比RNN模型更健壮，并且显示出更好的能力来有效解决过度拟合问题。此外，SRNN模型在检测少数类别的网络流量样本中具有更好的泛化能力。