The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy method for finding a feasible solution to a linear program (alternatively, for learning a threshold function.). In spite of its exponential worstcase complexity, it is often quite useful, in part due to its noise-tolerance and also its overall simplicity. In this paper, we show that a randomized version of the perceptron algorithm with periodic rescaling runs in polynomial-time. The resulting algorithm for linear programming has an elementary description and analysis.
We describe a new approach, called Strider, to Change and Configuration Management and Support (CCMS). Strider is a black-box approach: without relying on specifications, it uses state differencing to identify potential causes of differing program behaviors, uses state tracing to identify actual, run-time state dependencies, and uses statistical behavior modeling for noise filtering. Strider is a state-based approach: instead of linking vague, high-level descriptions and symptoms to relevant actions, it models management and support problems in terms of individual, named pieces of low-level configuration state and provides precise mappings to user-friendly information through a computer genomics database. We use troubleshooting of configuration failures to demonstrate that the Strider approach reduces problem complexity by several orders of magnitude, making root cause analysis possible.
Remote code injection exploits inflict a significant societal cost, and an active underground economy has grown up around these continually evolving attacks. We present a methodology for inferring the phylogeny, or evolutionary tree, of such exploits. We have applied this methodology to traffic captured at several vantage points, and we demonstrate that our methodology is robust to the observed polymorphism. Our techniques revealed non-trivial code sharing among different exploit families, and the resulting phylogenies accurately captured the subtle variations among exploits within each family. Thus, we believe our methodology and results are a helpful step to better understanding the evolution of remote code injection exploits on the Internet.
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