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Anomaly Attack Detection in Wireless Networks Using DCNN
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-}
}