Masquerade detection augmented with error analysis
A masquerade attack, in which one user impersonates another, may be one of the most serious forms of computer abuse. Automatic discovery of masqueraders is sometimes undertaken by detecting significant departures from normal user behavior, as represented by a user profile formed from system audit data. A major obstacle for this type of research is the difficulty in obtaining such system audit data, largely due to privacy concerns. An immense contribution in this regard has been made by Schonlau et al., who have made available UNIX command-line data from 50+ users collected over a number of months. Most of the research in this area has made use of this dataset, so this paper takes as its point of departure the Schonlau et al. dataset and a recent series of experiments with this data framed by the same researchers [1]. In extending that work with a new classification algorithm, a 56% improvement in masquerade detection was achieved at a corresponding false-alarm rate of 1.3%. In addition, encouraging results were obtained at a more realistic sequence length of 10 commands (as opposed to sequences of 100 commands used by Schonlau et al.). A detailed error analysis, based on an alternative configuration of the same data, reveals a serious flaw in this type of data which hinders masquerade detection and indicates some steps that need to be taken to improve future results. The error analysis also demonstrates the insights that can be gained by inspecting decision errors, instead of concentrating only on decision successes.
masquerade detection anomaly detection classifiers fraud detection naive bayes user command data