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An Intrusion-Detection Model

Published: 01 February 1987 Publication History

Abstract

A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. The model includes profiles for representing the behavior of subjects with respect to objects in terms of metrics and statistical models, and rules for acquiring knowledge about this behavior from audit records and for detecting anomalous behavior. The model is independent of any particular system, application environment, system vulnerability, or type of intrusion, thereby providing a framework for a general-purpose intrusion-detection expert system.

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  • (2025)A classifications framework for continuous biometric authentication (2018–2024)Computers and Security10.1016/j.cose.2024.104285150:COnline publication date: 1-Mar-2025
  • (2024)You cannot escape meProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699190(5179-5196)Online publication date: 14-Aug-2024
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Information & Contributors

Information

Published In

cover image IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering  Volume 13, Issue 2
Special issue on computer security and privacy
February 1987
173 pages
ISSN:0098-5589
Issue’s Table of Contents

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IEEE Press

Publication History

Published: 01 February 1987

Author Tags

  1. Abnormal behavior
  2. auditing
  3. intrusions
  4. monitoring
  5. profiles
  6. security
  7. statistical measures

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Cited By

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  • (2025)ENDEMIC: End-to-End Network Disruptions – Examining Middleboxes, Issues, and Countermeasures – A SurveyACM Computing Surveys10.1145/371637257:7(1-42)Online publication date: 21-Feb-2025
  • (2025)A classifications framework for continuous biometric authentication (2018–2024)Computers and Security10.1016/j.cose.2024.104285150:COnline publication date: 1-Mar-2025
  • (2024)You cannot escape meProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699190(5179-5196)Online publication date: 14-Aug-2024
  • (2024)Discriminative spatial-temporal feature learning for modeling network intrusion detection systemsJournal of Computer Security10.3233/JCS-22003132:1(1-30)Online publication date: 2-Feb-2024
  • (2024)The Future of Misuse DetectionCommunications of the ACM10.1145/368959667:11(27-28)Online publication date: 15-Oct-2024
  • (2024)Utilizing Threat Partitioning for More Practical Network Anomaly DetectionProceedings of the 29th ACM Symposium on Access Control Models and Technologies10.1145/3649158.3657046(83-91)Online publication date: 24-Jun-2024
  • (2024)RFG-HELAD: A Robust Fine-Grained Network Traffic Anomaly Detection Model Based on Heterogeneous Ensemble LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.340243919(5895-5910)Online publication date: 17-May-2024
  • (2024)Attenuating majority attack class bias using hybrid deep learning based IDS frameworkJournal of Network and Computer Applications10.1016/j.jnca.2024.103954230:COnline publication date: 18-Oct-2024
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  • (2024)SYNTROPYComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110327244:COnline publication date: 1-May-2024
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