Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs

Zhao, Caidan; Chen, Caiyun; Cai, Zhibiao; Shi, Mingxian; Du, Xiaojiang; Guizani, Mohsen

doi:10.1109/glocom.2018.8647973

Cited by 47 publications

(25 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…C. Generative Adversarial Networks 1) Classification based on Auxiliary Classifier Wasserstein GANs: The authors propose a RF-based UAV classification system based on Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGAN) in [125]. In this work, the authors collect wireless data from four different types of UAVs (including Phantom, Mi, Hubsan, and Xiro) using Agilent (DSO9404A) oscilloscope and antenna for indoor environment, and USRP N210 with CBX daughterboard for an outdoor environment.…”

Section: Inputmentioning

confidence: 99%

A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

Jagannath¹,

Jagannath²,

Kumar³

2022

Preprint

View full text Add to dashboard Cite

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

show abstract

Section: Inputmentioning

confidence: 99%

A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

Jagannath¹,

Jagannath²,

Kumar³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Ezuma et al [26] extracted RF-based features with the aid of FFT, and, for the first time, used a Markov-based model and a plain Bayesian decision mechanism for detecting RF signals from any source. Zhao et al [24] improved the auxiliary classifier GANs (AC-GAN) model by leveraging the Wasserstein GANs (WGAN) model. It simplified the recognition steps and can be applied in both indoor and outdoor environments.…”

Section: Related Workmentioning

confidence: 99%

“…Complex signal sources in noise-prone environments. Existing work typically collects and extracts UAV signals through analyzing physical signals, such as acoustic [17], [18], radar [19], [20], radio-frequency (RF) signal [21], [22], [23], [24],…”

Section: Introductionmentioning

confidence: 99%

BisSiam: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

Li¹,

Zhou²,

Cai³

et al. 2023

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposes BISSIAM, a novel framework that is capable of identifying UAV presence, types and operation modes. BISSIAM converts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experiment results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios. 91.4% accuracy can be achieved when BISSIAM is used for detecting the UAV type of the out-of-sample UAVs.

show abstract

“…The authors suggested that this framework could, in principle, be extended to large-scale channels, such as multiple-input/multiple-output. Zhao et al [99] studied the detection, tracking, and classification of Unnamed Aerial Vehicles (UAVs). They used an oscilloscope and antenna to collect wireless signals in an indoor environment with a sample rate of 20 GS/s.…”

Section: Physical Layermentioning

confidence: 99%

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

et al. 2021

View full text Add to dashboard Cite

show abstract

Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs

Cited by 47 publications

References 19 publications

A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

BisSiam: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

Contact Info

Product

Resources

About