The effectiveness of rate adaptation algorithms is an important determinant of 802.11 wireless network performance. The diversity of algorithms that has resulted from efforts to improve rate adaptation has introduced a new dimension of variability into 802.11 wireless networks, further complicating the already difficult task of understanding and debugging 802.11 performance. To assist with this task, in this paper we present and evaluate a methodology for accurately fingerprinting 802.11 rate adaptation algorithms. Our approach uses a Support Vector Machine (SVM)-based classifier that requires only simple passive measurements of 802.11 traffic. We demonstrate that careful conversion of raw packet traces into input features for SVM is necessary for achieving high classification accuracy. We tested our classifier on the four rate adaptation algorithms available in MadWifi, cards. The classifier performs with an accuracy of 95% – 100%. We also show that the classifier is robust over a variety of network conditions if the training data includes a sufficient sampling of the range of an algorithm's behavior.
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