Detecting selfish carrier-sense behavior in WiFi networks by passive monitoring

With the advent of programmability in radios, it is becoming easier for wireless network nodes to cheat to obtain an unfair share of the bandwidth. In this work we study the widely used 802.11 protocol and present a solution to detect selfish carrier-sensing behavior where a node raises the CCA (clear channel assessment) threshold for carriersensing, or simply does not sense carrier (possibly randomly to avoid detection). Our approach is based on detecting any asymmetry in carrier-sense behavior between node pairs andfinding multiple such witnesses to raise confidence. The approach is completely passive. It requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed by using a machine learning approach to infer carrier-sense relationships between network nodes. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability. The metric of selfishness used to estimate selfish behavior matches closely with actual degree of selfishness observed.