Anomaly Attack Detection in Wireless Networks Using DCNN

Dao, Van-Lan, 1987- (författare): Mälardalens universitet,Innovation och produktrealisering

Leander, Björn, 1978- (författare): Mälardalens universitet,Inbyggda system,ABB AB, Process Control Platform, Västerås, Sweden

(creator_code:org_t)

Institute of Electrical and Electronics Engineers Inc. 2022
2022
Engelska.
Ingår i: 2022 IEEE 8th World Forum on Internet of Things, WF-IoT 2022. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665491532

Relaterad länk:: https://urn.kb.se/re...; visa fler...; https://doi.org/10.1...; visa färre...

Abstract Ämnesord

Stäng

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.

NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Om ämnet

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