In IEEE 802.11 based ad hoc networks, by simply manipulating the back-off timers and/or wait times prior to transmission, malicious nodes can cause a drastically reduced allocation of bandwidth to well-behaved nodes. This can result in causing bandwidth starvation and hence, a denial of service to legitimate nodes. We propose a combination of deterministic and statistical methods that facilitate detection of such misbehavior. With our approach, each of the nodes is made aware of the pseudo-random sequences that dictate the back-off times of all its one-hop neighbors. A blatant violation of the timer is thus, immediately detected. In certain cases, a node may be unable to monitor the activities of its neighbor and therefore deterministically ascertain if the neighbor is misbehaving. To cope with such cases, we propose a statistical inference method, wherein based on an auto-regressive moving average (ARMA) of observations of the system state, a node is able to estimate if its neighbor is indulging in misbehavior. Simulation results show that with our methods, it is possible to detect a malicious node with a probability close to one. Furthermore, the probability of false alarms is lower than 1%.