Transfer and CNN-Based De-Authentication (Disassociation) DoS Attack Detection in IoT Wi-Fi Networks

Samson Gebresilassie, Joseph Rafferty, Luke Chen, Zhan Cui, Mamun Abu-Tair

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sen-sors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT envi-ronments. However, IoT networks are facing major security challenges due to various vulnerabili-ties, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detec-tion capabilities by adapting machine learning approaches, especially deep learning (DL). Howev-er, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tai-lored to suit IoT environments. The main purpose of this work was to overcome these limitations by designing a transfer learning (TL)- and convolutional neural network (CNN)-based IDS for de-authentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate our own dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive re-search were carried out with performance evaluation through multiple metrics and the results re-veal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive train-ing and evaluation of the proposed model, and comparative analysis with existing models, show that this work allows improved early detection and prevention of de-authentication and disasso-ciation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures.
Original languageEnglish
Article number3731
Pages (from-to)3731
Number of pages32
JournalElectronics
Volume12
Issue number17
Early online date4 Sept 2023
DOIs
Publication statusPublished online - 4 Sept 2023

Bibliographical note

Funding Information:
This research has been supported by the BT Ireland Innovation Centre (BTIIC) project, funded by BT, and Invest Northern Ireland.

Publisher Copyright:
© 2023 by the authors.

Keywords

  • de-authentication and disassociation attacks
  • intrusion detection system
  • convolutional neural network
  • transfer learning
  • Wi-Fi networks

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