Abstract-Initial work in ad hoc routing has considered only the problem of providing efficient mechanisms for finding paths in very dynamic networks, without considering security. Because of this, there are a number of attacks that can be used to manipulate the routing in an ad hoc network. In this paper, we describe these threats, specifically showing their effects on AODV and DSR. Our protocol, named Authenticated Routing for Ad hoc Networks (ARAN), uses public-key cryptographic mechanisms to defeat all identified attacks. We detail how ARAN can secure routing in environments where nodes are authorized to participate but untrusted to cooperate, as well as environments where participants do not need to be authorized to participate. Through both simulation and experimentation with our publicly-available implementation, we characterize and evaluate ARAN and show that it is able to effectively and efficiently discover secure routes within an ad hoc network.
We extend relevance modeling to the link detection task of Topic Detection and Tracking (TDT) and show that it substantially improves performance. Relevance modeling, a statistical language modeling technique related to query expansion, is used to enhance the topic model estimate associated with a news story, boosting the probability of words that are associated with the story even when they do not appear in the story. To apply relevance modeling to TDT, it had to be extended to work with stories rather than short queries, and the similarity comparison had to be changed to a modified form of Kullback-Leibler. We demonstrate that relevance models result in very substantial improvements over the language modeling baseline. We also show how the use of relevance modeling makes it possible to choose a single parameter for within-and cross-mode comparisons of stories.
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