Real-time Classification of Jamming Attacks against UAVs via on-board Software-defined Radio and Machine Learning-based Receiver Module-Reference-Cited by-同舟云学术

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Real-time Classification of Jamming Attacks against UAVs via on-board Software-defined Radio and Machine Learning-based Receiver Module

Published:2022-05-19 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Electro Information Technology (eIT)
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Author:

Price J.¹,Li Y.¹,Shamaileh K. Al¹,Niyaz Q.¹,Kaabouch N.²,Devabhaktuni V.³

Affiliation:

1. Purdue University Northwest,Electrical and Computer Engineering Department,Hammond,IN,USA,46323

2. University of North Dakota,School of Electrical Engineering and Computer Science,Grand Forks,ND,USA,58202

3. The University of Maine,Electrical and Computer Engineering Department,Orono,ME,USA,04469

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9813697/9813750/09813923.pdf?arnumber=9813923

Reference30 articles.

1. A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

2. DJAVAN: Detecting jamming attacks in Vehicle Ad hoc Networks

3. Solution of detecting jamming attacks in vehicle ad hoc networks

4. Machine Learning Approach for Multiple Misbehavior Detection in VANET

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1. A Self-Attention Residual Network for SAR Jamming Classification with Multi-Domain Feature Fusion;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Towards a Responsive Security Operations Center for UAVs;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

3. Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks;SoutheastCon 2023;2023-04-01

4. Low-Cost Raspberry-Pi-Based UAS Detection and Classification System Using Machine Learning;Aerospace;2022-11-22

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