Rolling preambles: Mitigating stealthy FO estimation attacks in OFDM-based 802.11 systems

Rahbari, Hanif; Krunz, Marwan

doi:10.1109/cns.2016.7860477

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…A PHY layer technique was developed in ref. [15] to mitigate the intelligent IEEE 802.11 reactive jammer that targeted the parts of the preamble used in CFO estimation [28]. Techniques included cyclically shifting the short sequence in time, randomly selecting a different sequence out of 4 static options, or changing the sequence repetitions used by the receiver for estimation.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In all of the aforementioned studies, analysis was limited to simulation and would benefit from experimental evaluation to provide a greater sense of realism to the communication devices considered. Hardware-based systems have focused on performing jamming mitigation primarily for lowrate hostile military applications [13,14] or for very specific jammer types [15,16]. A more generalised solution must beThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.…”

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confidence: 99%

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Mitigating RF jamming attacks at the physical layer with machine learning

Jacovic

Rey

Mainland³

et al. 2022

IET Communications

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Wireless communication devices must be protected from malicious threats, including active jamming attacks, due to the widespread use of wireless systems throughout our everyday 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. INTRODUCTIONWireless communication systems exist widely throughout commercial, industrial, military, and consumer domains. Communication links must be reliable and well-protected from external threats, in particular for medical and vehicular applications, which are critical to human well-being and safety. Radio frequency (RF) jammers intentionally disrupt the RF medium with active attacks to maliciously impact the performance of legitimate communication devices. Mitigation techniques in the presence of jamming have predominately been evaluated in the past through theoretical and simulation based studies [1-7].Recent approaches have leveraged neural networks [8] and reinforcement learning [9][10][11][12] to circumvent the effects of a jammer. In all of the aforementioned studies, analysis was limited to simulation and would benefit from experimental evaluation to provide a greater sense of realism to the communication devices considered. Hardware-based systems have focused on performing jamming mitigation primarily for lowrate hostile military applications [13,14] or for very specific jammer types [15,16]. A more generalised solution must beThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%