Intrusion detection complements prevention mechanisms, such as firewalls, cryptography, and authentication, to capture intrusions into an information system while they are acting on the information system. Our study investigates a multivariate quality control technique to detect intrusions by building a long-term profile of normal activities in information systems (norm profile) and using the norm profile to detect anomalies. The multivariate quality control technique is based on Hotelling's T/sup 2/ test that detects both counterrelationship anomalies and mean-shift anomalies. The performance of the Hotelling's T/sup 2/ test is examined on two sets of computer audit data: a small data set and a large multiday data set. Both data sets contain sessions of normal and intrusive activities. For the small data set, the Hotelling's T/sup 2/ test signals all the intrusion sessions and produces no false alarms for the normal sessions. For the large data set, the Hotelling's T/sup 2/ test signals 92 percent of the intrusion sessions while producing no false alarms for the normal sessions. The performance of the Hotelling's T/sup 2/ test is also compared with the performance of a more scalable multivariate technique-a chi-squared distance test.

This paper presents a series of studies on probabilistic properties of activity data in an information system for detecting intrusions into the information system. Various probabilistic techniques of intrusion detection, including decision tree, Hotelling's T/sup 2/ test, chi-square multivariate test, and Markov chain are applied to the same training set and the same testing set of computer audit data for investigating the frequency property and the ordering property of computer audit data. The results of these studies provide answers to several questions concerning which properties are critical to intrusion detection. First, our studies show that the frequency property of multiple audit event types in a sequence of events is necessary for intrusion detection. A single audit event at a given time is not sufficient for intrusion detection. Second, the ordering property of multiple audit events provides additional advantage to the frequency property for intrusion detection. However, unless the scalability problem of complex data models taking into account the ordering property of activity data is solved, intrusion detection techniques based on the frequency property provide a viable solution that produces good intrusion detection performance with low computational overhead.