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Journal article
Mitigating RF jamming attacks at the physical layer with machine learning
IET communications
06 Oct 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Wireless communication devices must be protected from malicious threats, including active jamming attacks, due to the widespread use of wireless systems throughout our every-day lives. Jamming mitigation techniques are predominately evaluated through simulation or with hardware for very specific jamming conditions. In this paper, an experimental software defined radio-based RF jamming mitigation platform which performs online jammer classification and leverages reconfigurable beam-steering antennas at the physical layer is introduced. A ray-tracing emulation system is presented and validated to enable hardware-in-the-loop jamming experiments of complex outdoor and mobile site-specific scenarios. Random forests classifiers are trained based on over-the-air collected data and integrated into the platform. The mitigation system is evaluated for both over-the-air and ray-tracing emulated environments. The experimental results highlight the benefit of using the jamming mitigation system in the presence of active jamming attacks.
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Details
- Title
- Mitigating RF jamming attacks at the physical layer with machine learning
- Creators
- Marko Jacovic - Drexel Univ, Dept Elect & Comp Engn, 3141 Chestnut St, Philadelphia, PA 19104 USAXaime Rivas Rey - Drexel Univ, Dept Elect & Comp Engn, 3141 Chestnut St, Philadelphia, PA 19104 USAGeoffrey Mainland - Drexel UniversityKapil R. Dandekar - Drexel Univ, Dept Elect & Comp Engn, 3141 Chestnut St, Philadelphia, PA 19104 USA
- Publication Details
- IET communications
- Publisher
- Inst Engineering Technology-Iet
- Number of pages
- 17
- Grant note
- CCF-1717088; CNS-1730140 / National Science Foundation; National Science Foundation (NSF) P200A180082 / Office of Postsecondary Education
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Computer Science
- Web of Science ID
- WOS:000864513900001
- Scopus ID
- 2-s2.0-85139410329
- Other Identifier
- 991019186625504721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Electrical & Electronic