Recurrent Neural Network Model for IoT and Networking Malware Threat Detection

Security of networking in cyber-physical systems is an important feature in recent computing. Information that comes to the network needs preevaluation. Our solution presented in this article is based on deep learning model developed for network traffic analysis of various Internet of things solutions. At the level of firewall or gateway, information about current connection is gathered for the recurrent neural network. The model evaluates this information and forwards decision back to the firewall to take security actions if needed. In the research, we have tested our solution on two open datasets. The results confirm that our model is very efficient in recognition of potential threats reaching above 99% of accuracy even in a case of reduced number of evaluated networking features.

Automated summary

The security of networking in cyber-physical systems is crucial, and this article proposes a solution based on a deep learning model for analyzing network traffic. The model efficiently evaluates information and makes security decisions, achieving over 99% accuracy even with a reduced number of features evaluated.