We derive efficient algorithms for both detecting and representing matchings in lopsided bipartite graphs; such graphs have so many nodes on one side that it is infeasible to represent them in memory or to identify matchings using standard approaches. Detecting and representing matchings in lopsided bipartite graphs is important for allocating and delivering guaranteed-placement display ads, where the corresponding bipartite graph of interest has nodes representing advertisers on one side and nodes representing web-page impressions on the other; real-world instances of such graphs can have billions of impression nodes. We provide theoretical guarantees for our algorithms, and in a real-world advertising application, we demonstrate the feasibility of our detection algorithms.

We consider the problem of efficiently designing sets (codes) of equal-length DNA strings (words) that satisfy certain combinatorial constraints. This problem has numerous motivations including DNA self-assembly and DNA computing. Previous work has extended results from coding theory to obtain bounds on code size for new biologically motivated constraints and has applied heuristic local search and genetic algorithm techniques for code design. This article proposes a natural optimization formulation of the DNA code design problem in which the goal is to design n strings that satisfy a given set of constraints while minimizing the length of the strings. For multiple sets of constraints, we provide simple randomized algorithms that run in time polynomial in n and any given constraint parameters, and output strings of length within a constant factor of the optimal with high probability. To the best of our knowledge, this work is the first to consider this type of optimization problem in the context of DNA code design.

Zero-day polymorphic worms pose a serious threat to the security of Internet infrastructures. Given their rapid propagation, it is crucial to detect them at edge networks and automatically generate signatures in the early stages of infection. Most existing approaches for automatic signature generation need host information and are thus not applicable for deployment on high-speed network links. In this paper, we propose Hamsa, a network-based automated signature generation system for polymorphic worms which is fast, noise-tolerant and attack-resilient. Essentially, we propose a realistic model to analyze the invariant content of polymorphic worms which allows us to make analytical attack-resilience guarantees for the signature generation algorithm. Evaluation based on a range of polymorphic worms and polymorphic engines demonstrates that Hamsa significantly outperforms Polygraph (J. Newsome et al., 2005) in terms of efficiency, accuracy, and attack resilience