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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%.

The increasing interest in time series data mining in the last decade has had surprisingly little impact on real world medical applications. Real world practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that the data mining community has made available. In this work, we attempt to address this problem by introducing a simple parameter-light tool that allows users to efficiently navigate through large collections of time series. Our system has the unique advantage that it can be embedded directly into any standard graphical user interfaces, such as Microsoft Windows, thus making deployment easier. Our approach extracts features from a time series of arbitrary length and uses information about the relative frequency of these features to color a bitmap in a principled way. By visualizing the similarities and differences within a collection of bitmaps, a user can quickly discover clusters, anomalies, and other regularities within their data collection. We demonstrate the utility of our approach with a set of comprehensive experiments on real datasets from a variety of medical domains, including ECGs and EEGs.