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An integrated pattern recognition approach for intrusion detection.
Biomed Sci Instrum. 2002; 38:447-52.BS

Abstract

Intrusion detection systems (IDS) attempt to address the vulnerability of computer-based systems for abuse by insiders and to penetration by outsiders. An IDS is required to examine an enormous amount of data generated by computer networks to assist in the abuse detection process. Thus, there is a need to develop automated tools that address these requirements to assist system operators in the detection of violations of existing security policies. In this research, an automated IDS is proposed for insider threats in a distributed system. The proposed IDS functions as an anomaly detector for insider system operations based on the analysis of the system's log files. The approach integrates dynamic programming and adaptive resonance theory (ART1) clustering. The integrated approach aligns sequences of log events with prototypical sequences of events for performing tasks and classifies the aligned sequences for intrusion detection. The system examined for this research is a Boots System for controlling the movement of boots from one place to another under specific security restrictions related to the boot orders. We present the proposed model, the results achieved and the analysis of an implemented prototype.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, 127 Emerson Electric Co. Hall, University of Missouri-Rolla, Rolla, MO 65409, USA.No affiliation info availableNo affiliation info available

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

12085648

Citation

Pandit, Amod, et al. "An Integrated Pattern Recognition Approach for Intrusion Detection." Biomedical Sciences Instrumentation, vol. 38, 2002, pp. 447-52.
Pandit A, Stanley RJ, McMillin B. An integrated pattern recognition approach for intrusion detection. Biomed Sci Instrum. 2002;38:447-52.
Pandit, A., Stanley, R. J., & McMillin, B. (2002). An integrated pattern recognition approach for intrusion detection. Biomedical Sciences Instrumentation, 38, 447-52.
Pandit A, Stanley RJ, McMillin B. An Integrated Pattern Recognition Approach for Intrusion Detection. Biomed Sci Instrum. 2002;38:447-52. PubMed PMID: 12085648.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An integrated pattern recognition approach for intrusion detection. AU - Pandit,Amod, AU - Stanley,R Joe, AU - McMillin,Bruce, PY - 2002/6/28/pubmed PY - 2002/11/26/medline PY - 2002/6/28/entrez SP - 447 EP - 52 JF - Biomedical sciences instrumentation JO - Biomed Sci Instrum VL - 38 N2 - Intrusion detection systems (IDS) attempt to address the vulnerability of computer-based systems for abuse by insiders and to penetration by outsiders. An IDS is required to examine an enormous amount of data generated by computer networks to assist in the abuse detection process. Thus, there is a need to develop automated tools that address these requirements to assist system operators in the detection of violations of existing security policies. In this research, an automated IDS is proposed for insider threats in a distributed system. The proposed IDS functions as an anomaly detector for insider system operations based on the analysis of the system's log files. The approach integrates dynamic programming and adaptive resonance theory (ART1) clustering. The integrated approach aligns sequences of log events with prototypical sequences of events for performing tasks and classifies the aligned sequences for intrusion detection. The system examined for this research is a Boots System for controlling the movement of boots from one place to another under specific security restrictions related to the boot orders. We present the proposed model, the results achieved and the analysis of an implemented prototype. SN - 0067-8856 UR - https://www.unboundmedicine.com/medline/citation/12085648/abstract/An_integrated_pattern_recognition_approach_for_intrusion_detection_ DB - PRIME DP - Unbound Medicine ER -
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