Recurrent Neural Network Model for IoT and Networking Malware Threat Detection

Marcin Wozniak; Jakub Silka; Michal Wieczorek; Mubarak Alrashoud

doi:10.1109/TII.2020.3021689

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Journal article

Recurrent Neural Network Model for IoT and Networking Malware Threat Detection

Marcin Wozniak, Jakub Silka, Michal Wieczorek and Mubarak Alrashoud

IEEE transactions on industrial informatics, Vol.17(8), pp.5583-5594

01/08/2021

DOI: https://doi.org/10.1109/TII.2020.3021689

Abstract

Androids

Humanoid robots

Informatics

Instruction sets

Internet traffic

Malware

malware detection

recurrent neural network (RNN)

Recurrent neural networks

Security

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.

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Title: Recurrent Neural Network Model for IoT and Networking Malware Threat Detection
Creators - without role: Marcin Wozniak - Silesian University of Technology
Jakub Silka - Silesian University of Technology
Michal Wieczorek - Silesian University of Technology
Mubarak Alrashoud - King Saud University
Publication Details: IEEE transactions on industrial informatics, Vol.17(8), pp.5583-5594
Publisher: IEEE
Grant note: Deanship of Scientific Research, King Saud University (10.13039/501100011665) Vice Deanship of Scientific Research Chairs: Chair of Smart Technologies
Identifiers: 9949894208331
Academic Unit: King Saud University
Language: English
Resource Type: Journal article