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A new approach, based on the k-Nearest Neighbor (kNN) classifier, is used to classify program
behavior as normal or intrusive.
Short sequences of system calls have been used by others to characterize a program's normal behavior
separate databases of short system call sequences have to be built for different programs, and learning
program profiles involves time-consuming training and testing processes. With the kNN classifier, the
frequencies of system calls are used to describe the program behavior. Text categorization techniques are
adopted to convert each process to a vector and calculate the similarity between two program
activities. Since there is no need to learn individual program profiles separately, the calculation
largely reduced. Preliminary experiments with 1998 DARPA BSM audit data show that the kNN classifier
can effectively detect intrusive attacks and achieve a low false positive rate.