Stacked recurrent neural network for botnet detection in smart homes.

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. [Display omitted] • Multiple RNN are stacked to detect botnet attacks in smart homes. • Performance of RNN and SRNN is evaluated with Bot-IoT dataset. • Binary and multi-class classification scenarios are considered. • SRNN outperformed RNN and state-of-the-art models in all scenarios. [ABSTRACT FROM AUTHOR]