Multivariate Statistical Analysis of Audit Trails for Host-Based Intrusion Detection

  • Authors:
  • Nong Ye;Syed Masum Emran;Qiang Chen;Sean Vilbert

  • Affiliations:
  • -;-;-;-

  • Venue:
  • IEEE Transactions on Computers
  • Year:
  • 2002

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Abstract

Intrusion detection complements prevention mehcanisms, 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 \rm T^2 test that detects both counterrelationship anomalies and mean-shift anomalies. The performance of the Hotelling's \rm T^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 \rm T^2 test signals all the intrusion sessions and produces no false alarms for the normal sessions. For the large data set, the Hotelling's \rm T^2 test signals 92 percent of the intrusion sessions while producing no false alarms for the normal sessions. The performance of the Hotelling's \rm T^2 test is also compared with the performance of a more scalable multivariate technique驴a chi-squared distance test.