Jamming-resistant rate control in Wi-fi networks
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Recent experimental studies reveal that several well-known and widely deployed rate adaptation algorithms (RAAs) in 802.11 WLANs are vulnerable to selective jamming attacks. However, previous work resorts to complex jamming strategies that are hard to implement and does not provide applicable solutions to this problem. In this work, we analyze the vulnerabilities of existing RAAs to simple jamming attacks and propose judicious use of randomization to address this problem. We in- troduce a theoretical framework based on a bursty periodic jamming model to analyze the vulnerabilities of popular RAAs, such as ARF, AARF, Onoe, and SampleRate. Our parameterized analysis shows that a jamming rate of 10% or below is sufficient to bring the throughput of these algorithms below the base rate of 1 Mb/s. Thereafter, we propose a new algorithm, called Randomized ARF (RARF), that is resistant to jamming attacks. We derive a closed-form lower bound on the minimum jamming rate required to keep the throughput of RARF below the base rate. We also analyze a recently proposed randomized RAA called Minstrel and compare its performance to RARF under alternative jamming models. Finally, we conduct ns-3 simulations implementing the various RAAs and jam- ming strategies for an IEEE 802.11g WLAN. Our simulations validate the jamming strategies under different channel models and show that the minimum jamming rate required against RARF is about 33%.
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