A Comparative Study of Attribute Selection Algorithms on Intrusion Detection System in UAVs: A Case Study of UKM-IDS20 Dataset

Mohammed, Ahmed Burhan; Fourati, Lamia Chaari; Fakhrudeen, Ahmed M.

doi:10.1007/978-3-031-31108-6_3

Cited by 1 publication

(1 citation statement)

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“…In [10], the authors introduced the University Kebangsaan Malaysia Intrusion Detection System 20 (UKM-IDS20) dataset, which was created particularly for intrusion detection in UAVs to solve security issues around UAVs. Their research examines the effect of feature selection on IDS for UAV networks in light of the significance of feature selection in improving the effectiveness of IDS.…”

Section: Related Workmentioning

confidence: 99%

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

M. Abdulghani,

L. Walters

et al. 2022

Journal of Cyber Security

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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 realworld scenario.

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Section: Related Workmentioning

confidence: 99%