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Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

Department of Electrical & Computer Engineering and Computer Science, Jackson State University (JSU), Jackson, 39217, USA

* Corresponding Author: Khalid H. Abed. Email: email

Journal of Cyber Security 2022, 4(4), 287-298.


Unmanned Aerial Vehicles (UAVs) proliferate quickly and play a significant part in crucial tasks, so it is important to protect the security and integrity of UAV communication channels. Intrusion Detection Systems (IDSs) are required to protect the UAV communication infrastructure from unauthorized access and harmful actions. In this paper, we examine a new approach for enhancing intrusion detection in UAV communication channels by utilizing the Long Short-Term Memory network (LSTM) combined with the Synthetic Minority Oversampling Technique (SMOTE) algorithm, and this integration is the binary classification method (LSTM-SMOTE). We successfully achieved 99.83% detection accuracy by using the proposed approach and the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset 2017 (CICIDS2017) dataset. We demonstrated the efficiency of LSTM-SMOTE in defending UAV communication channels against possible attacks and bolstering the overall security posture through the use of a real-world scenario.


Cite This Article

A. M. Abdulghani, M. M. Abdulghani, W. L. Walters and K. H. Abed, "Improving intrusion detection in uav communication using an lstm-smote classification method," Journal of Cyber Security, vol. 4, no.4, pp. 287–298, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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