You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Anomaly Attack Detection in Wireless Networks Using DCNN

Fulltext:


Research group:


Publication Type:

Conference/Workshop Paper

Venue:

IEEE 8th World Forum on Internet of Things


Abstract

The use of wireless devices in industrial sectors has increased due to its various advantages related to cost and flexibility. However, legitimate wireless communication systems are vulnerable to cybersecurity attacks, due to its inherent open nature. Detection of rogue devices therefore plays a crucial role in critical wireless applications.In this paper we design a deep convolutional neural network (DCNN) to classify legitimate and rogue devices using raw IQ samples as input data. An algorithm is presented to find the optimal number of convolutional layers and number of filters for each layer under an accuracy constraint, in order to enable fast prediction time. Furthermore, we investigate how wireless channel models affect the accuracy and prediction time of the designed DCNN model. Our obtained results are benchmarked against previous DCNN models. Moreover, we discuss how the systems should react to a detected rogue device, considering the IEC 62443 standard.

Bibtex

@inproceedings{Dao6553,
author = {Van Lan Dao and Bj{\"o}rn Leander},
title = {Anomaly Attack Detection in Wireless Networks Using DCNN},
month = {October},
year = {2022},
booktitle = {IEEE 8th World Forum on Internet of Things},
url = {http://www.es.mdu.se/publications/6553-}
}