by Jacovic, Marko, Rey, Xaime Rivas, Mainland, Geoffrey and Dandekar, Kapil R.
Abstract:
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.
Reference:
Mitigating RF jamming attacks at the physical layer with machine learning (Jacovic, Marko, Rey, Xaime Rivas, Mainland, Geoffrey and Dandekar, Kapil R.), In IET Communications, 2022.
Bibtex Entry:
@article{https://doi.org/10.1049/cmu2.12461,
author = {Jacovic, Marko and Rey, Xaime Rivas and Mainland, Geoffrey and Dandekar, Kapil R.},
title = {Mitigating RF jamming attacks at the physical layer with machine learning},
journal = {IET Communications},
year = {2022},
month = Oct,
doi = {https://doi.org/10.1049/cmu2.12461},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cmu2.12461},
abstract = {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.}
}