Reliability and quality of service from information systems has been threatened by cyber intrusions. To protect information systems from intrusions and thus assure reliability and quality of service, it is highly desirable to develop techniques that detect intrusions. Many intrusions manifest in anomalous changes in intensity of events occurring in information systems. In this study, we apply, test, and compare two EWMA techniques to detect anomalous changes in event intensity for intrusion detection: EWMA for autocorrelated data and EWMA for uncorrelated data. Different parameter settings and their effects on performance of these EWMA techniques are also investigated to provide guidelines for practical use of these techniques.

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.