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Intrusion Detection Applying Machine Learning to Solaris Audit Data

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D. EndlerAuthors Info & Claims

ACSAC '98: Proceedings of the 14th Annual Computer Security Applications Conference

Page 268

Published: 07 December 1998 Publication History

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Nagaraja AAljawarneh SS PLara Torralbo JAljawarneh S(2018)PAREEKSHAProceedings of the First International Conference on Data Science, E-learning and Information Systems10.1145/3279996.3280032(1-6)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3279996.3280032
Shah SIssac B(2018)Performance comparison of intrusion detection systems and application of machine learning to Snort systemFuture Generation Computer Systems10.1016/j.future.2017.10.01680:C(157-170)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.future.2017.10.016
Shen YLiu HWang YChen ZSun G(2016)A novel isolation-based outlier detection methodProceedings of the 14th Pacific Rim International Conference on Trends in Artificial Intelligence10.1007/978-3-319-42911-3_37(446-456)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1007/978-3-319-42911-3_37
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Intrusion Detection Applying Machine Learning to Solaris Audit Data
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Systems security
    1. Operating systems security

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ACSAC '98: Proceedings of the 14th Annual Computer Security Applications Conference

December 1998

ISBN:0818687894

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IEEE Computer Society

United States

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Published: 07 December 1998

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Nagaraja AAljawarneh SS PLara Torralbo JAljawarneh S(2018)PAREEKSHAProceedings of the First International Conference on Data Science, E-learning and Information Systems10.1145/3279996.3280032(1-6)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3279996.3280032
Shah SIssac B(2018)Performance comparison of intrusion detection systems and application of machine learning to Snort systemFuture Generation Computer Systems10.1016/j.future.2017.10.01680:C(157-170)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.future.2017.10.016
Shen YLiu HWang YChen ZSun G(2016)A novel isolation-based outlier detection methodProceedings of the 14th Pacific Rim International Conference on Trends in Artificial Intelligence10.1007/978-3-319-42911-3_37(446-456)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1007/978-3-319-42911-3_37
Shu XYao DRyder B(2015)A Formal Framework for Program Anomaly DetectionProceedings of the 18th International Symposium on Research in Attacks, Intrusions, and Defenses - Volume 940410.1007/978-3-319-26362-5_13(270-292)Online publication date: 2-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-26362-5_13
Shahrestani S(2009)Employing artificial immunology and approximate reasoning models for enhanced network intrusion detectionWSEAS Transactions on Information Science and Applications10.5555/1512690.15126956:2(190-200)Online publication date: 1-Feb-2009
https://dl.acm.org/doi/10.5555/1512690.1512695
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Yin QZhang RLi XWhite JSheng H(2004)An new intrusion detection method based on linear predictionProceedings of the 3rd international conference on Information security10.1145/1046290.1046323(160-165)Online publication date: 14-Nov-2004
https://dl.acm.org/doi/10.1145/1046290.1046323
Pusara MBrodley CBrodley CChan PLippman RYurcik B(2004)User re-authentication via mouse movementsProceedings of the 2004 ACM workshop on Visualization and data mining for computer security10.1145/1029208.1029210(1-8)Online publication date: 29-Oct-2004
https://dl.acm.org/doi/10.1145/1029208.1029210
Michael CGhosh A(2002)Simple, state-based approaches to program-based anomaly detectionACM Transactions on Information and System Security10.1145/545186.5451875:3(203-237)Online publication date: 1-Aug-2002
https://dl.acm.org/doi/10.1145/545186.545187
Ghosh ASchwartzbard ASchatz M(1999)Learning program behavior profiles for intrusion detectionProceedings of the 1st conference on Workshop on Intrusion Detection and Network Monitoring - Volume 110.5555/1267880.1267886(6-6)Online publication date: 9-Apr-1999
https://dl.acm.org/doi/10.5555/1267880.1267886
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Data mining and machine learning-Towards reducing false positives in intrusion detection

A Survey on Intrusion Detection and Prevention Systems

Network intrusion detection

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