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This paper discusses the use of genetic algorithms (GAs) for automatic software test data generation. This research extends previous work on dynamic test data generation where the problem of test data generation is reduced to one of minimizing a function Miller and Spooner, 1976, Korel, 1990]. In our work, the function is minimized by using one of two(More)
This article describes variants of two state-based intrusion detection algorithms from Michael and Ghosh [2000] and Ghosh et al. [2000], and gives experimental results on their performance. The algorithms detect anomalies in execution audit data. One is a simply constructed finite-state machine, and the other two monitor statistical deviations from normal(More)
In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, many attempts have been made to automate the process. Random test data generation consists of generating test inputs at random, in the hope that(More)
In practice, most computer intrusions begin by misusing programs in clever ways to obtain unauthorized higher levels of privilege. One eective way t o d e t e c t i n trusive activity before system damage is perpetrated is to detect misuse of privileged programs in real-time. In this paper, we describe three machine learning algorithms that learn the normal(More)
Application-based anomaly detectors construct a base-line model of normal application behavior, and deviations from that behavior are interpreted as signs of a possible intrusion. But current anomaly detectors monitor application behavior at a high level of detail, and many irrelevant variations in that behavior can cause false alarms. This paper discusses(More)