Two-stage IDS for IoT using layered machine- and deep-learning models

ABSTRACT

The ever-growing integration of Internet-of-Things (IoT) devices into our daily lives provides us with a level of convenience never before seen. However, the effects of attacks on these devices can be devastating. The discrete, low-powered nature of IoT devices makes their security a difficult problem to solve. To provide a solution, this work proposes a two-stage Intrusion Detection Systems (IDS) using layered machine and deep learning models. The potential benefits of the system are examined and the results presented show a reduction of threat detection/identification time of 0.51 s on average and an increase of threat classification F1-Score by 0.05. 

KEYWORDS:

• Security
• IoT
• machine learning
• BoT-IoT
• Two-stage classifier
• Multi-stage classifier

Acknowledgments

The authors would like to thank Absa for their funding of this research.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, A. van der Walt, upon reasonable request.

Additional information

Funding

This work was supported by the Absa Bank Limited;