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We present a general and systematic method for neural network design based on the genetic algorithm. The technique works in conjunction with network learning rules, addressing aspects of the network's gross architecture, connectivity, and learning rule parameters. Networks can be optimiled for various application-specific criteria, such as learning speed,(More)
We report on the results of applying classical planning techniques to the problem of analyzing computer network vul-nerabilities. Specifically, we are concerned with the generation of Adversary Courses of Action, which are extended sequences of exploits leading from some initial state to an attacker's goal. In this application, we have demonstrated the(More)
A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database(More)
A serious problem in mining industrial data bases is that they are often incomplete, and a significant amount of data is missing, or erroneously entered. This paper explores the use of machine-learning based alternatives to standard statistical data completion (data imputation) methods, for dealing with missing data. We have approached the data completion(More)
Coordinating multiple overlapping defense mechanisms , at differing levels of abstraction, is fraught with the potential for misconfiguration, so there is strong motivation to generate policies for those mechanisms from a single specification in order to avoid that risk. This paper presents our experience and the lessons learned as we developed, validated(More)
The insider threat has assumed increasing importance as our dependence on critical cyber information infrastructure has increased. In this paper we describe an approach for thwarting and attributing insider attacks. The Sense, Prepare, Detect, and React (SPDR) approach utilizes both a highly intelligent software reasoning system to anticipate, recognize,(More)
As networked systems become more complex, and they support more critical applications, there is a compelling need to augment the Red Team approach to vulnerability analysis with more formal, automated methods. Artificial Intelligence (AI) Planning, with its well-developed theory and rich set of tools, offers an attractive approach. By adopting this approach(More)