A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs

Author:

Pawlak Jered1,Li Yuchen1,Price Joshua1,Wright Matthew1,Al Shamaileh Khair1,Niyaz Quamar1,Devabhaktuni Vijay1

Affiliation:

1. Purdue University Northwest

Publisher

ACM

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Watch the Skies: A Study on Drone Attack Vectors, Forensic Approaches, and Persisting Security Challenges;Future Internet;2024-07-13

2. DroneDefGANt: A Generative AI-Based Approach for Detecting UAS Attacks and Faults;ICC 2024 - IEEE International Conference on Communications;2024-06-09

3. Deep Learning Models as Moving Targets to Counter Modulation Classification Attacks;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

4. On Protection of the Next-Generation Mobile Networks Against Adversarial Examples;Artificial Intelligence for Security;2024

5. MAG-JAM: Jamming Detection via Magnetic Emissions;Lecture Notes in Computer Science;2024

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