GUIDE: GAN-based UAV IDS Enhancement

With the development of information technology, many devices are connected and automated by networks. Unmanned Areal Vehicles (UAVs), commonly known as drones, are one of the most popular devices that can perform various tasks. However, the risk of cyberattacks on UAVs is increasing as UAV utilizati...

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Bibliographic Details
Published inComputers & security Vol. 147; p. 104073
Main Authors Yoo, Jeong Do, Kim, Haerin, Kim, Huy Kang
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.12.2024
Subjects
Cybersecurity
Data augmentation
Drone
GAN
IDS
Intrusion detection system
MAVLink
UAV
GAN
Intrusion detection system
MAVLink
Data augmentation
IDS
Cybersecurity
UAV
Drone
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Summary:With the development of information technology, many devices are connected and automated by networks. Unmanned Areal Vehicles (UAVs), commonly known as drones, are one of the most popular devices that can perform various tasks. However, the risk of cyberattacks on UAVs is increasing as UAV utilization grows. These cyberattacks can cause serious safety problems, such as crashes. Therefore, it is essential to detect these attacks and take countermeasures. As a countermeasure, intrusion detection system (IDS) is widely adopted. To implement IDS for UAVs, it should be lightweight and be able to detect unknown attacks as a requirement. We propose GAN-based UAV IDS Enhancement (GUIDE) to meet the requirements. The GUIDE employs a generative adversarial network (GAN) for integer-valued sequence data augmentation to enhance an IDS’s performance on known and unknown attacks. We used five GANs: SeqGAN, MaskGAN, RankGAN, StepGAN, and LeakGAN; we used four non-learning augmentation methods for the comparative experiment: oversampling, undersampling, noise addition, and random generation. The experimental results demonstrated that the synthetic data generated by GANs improved the detection of known attacks (up to 37 percentage points) and unknown attacks (up to 30 percentage points) while maintaining stable IDS performance. We also analyzed the synthetic data by employing Jensen–Shannon divergence, synthetic ranking agreement, and visualization; we confirmed that the synthetic data contained the characteristics of real data and could be used for training the IDS.
ISSN:0167-4048
DOI:10.1016/j.cose.2024.104073