Group intrusion detection in the Internet of Things using a hybrid recurrent neural network

This paper introduces a novel framework for identifying a group of intrusions in the context of the Internet of Things (IoT). It combines both deep learning and decomposition. A set of data is first collected and a recurrent neural network is used to estimate the different individual intrusions. These individual intrusions are then used to identify a group of outliers based on a decomposition strategy. As case studies, the proposed solutions have been experimentally evaluated using two computer network intrusion datasets, namely (1) IDS 2018, and (2) LUFlow. The results show the benefits of the proposed framework and clear superiority in comparison to the state-of-the-art approaches.

Automated summary

This paper presents a novel framework that combines deep learning and decomposition for identifying a group of intrusions in the IoT context. By collecting data and using a recurrent neural network to estimate individual intrusions, the framework then identifies outliers based on a decomposition strategy. Experimental evaluation using two intrusion datasets demonstrates the superiority of the proposed framework over state-of-the-art approaches.