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An evolutionary multi-agent approach to anomaly detection and cyber defense

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Marco Carvalho,

Carlos PerezAuthors Info & Claims

CSIIRW '11: Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research

Article No.: 28, Page 1

https://doi.org/10.1145/2179298.2179329

Published: 12 October 2011 Publication History

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References

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Snort. http://www.snort.org/.

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S. Axelsson. Intrusion detection systems: A survey and taxonomy. 2000.

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S. Axelsson. The base-rate fallacy and the difficulty of intrusion detection. ACM Transactions on Information and System Security (TISSEC), 3(3):186-205, 2000.

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M. Carvalho and C. M. Teng. Automatic discovery of attack messages and pre- and postconditions automatic discovery of attack messages and pre- and post-conditions for attack graph generation. In E. L. Armistead, editor, 5th International Conference on Information Warfare and Security (ICIW), pages 378-388, Wright-Patterson AFB, Ohio, USA, April 8-9 2010. AFRL, Academic Publishing Limited.

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J. Hartigan and M. Wong. A k-means clustering algorithm. JR Stat. Soc., Ser. C, 28:100-108, 1979.

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D. Kim, H. Nguyen, and J. Park. Genetic algorithm to improve SVM based network intrusion detection system. In Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on, volume 2, pages 155-158. IEEE, 2005.

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C. Kruegel, D. Mutz, W. Robertson, and F. Valeur. Bayesian event classification for intrusion detection. 2003.

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A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the Third SIAM International Conference on Data Mining, volume 3, 2003.

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R. Lippmann, J. Haines, D. Fried, J. Korba, and K. Das. The 1999 DARPA off-line intrusion detection evaluation. Computer Networks, 34(4):579-595, 2000.

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S. Mukkamala, G. Janoski, and A. Sung. Intrusion detection using neural networks and support vector machines. In Proceedings of IEEE international joint conference on neural networks, volume 1702, 2002.

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L. Portnoy, E. Eskin, and S. Stolfo. Intrusion detection with unlabeled data using clustering. In Proceedings of ACM CSS Workshop on Data Mining Applied to Security, Philadelphia, PA, 2001.

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Y. Qiao, X. Xin, Y. Bin, and S. Ge. Anomaly intrusion detection method based on HMM. Electronics Letters, 38(13):663-664, 2002.

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G. Schwarz. Estimating the dimension of a model. The annals of statistics, 6(2):461-464, 1978.

Cited By

Aiyanyo ISamuel HLim H(2020)A Systematic Review of Defensive and Offensive Cybersecurity with Machine LearningApplied Sciences10.3390/app1017581110:17(5811)Online publication date: 22-Aug-2020
https://doi.org/10.3390/app10175811
Saeed ISelamat ARohani MKrejcar OChaudhry J(2020)A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion DetectionIEEE Access10.1109/ACCESS.2020.30274638(180184-180209)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3027463
Zincir-Heywood NKayacik G(2017)Evolutionary computation in network management and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067726(1094-1112)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3067726
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CSIIRW '11: Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research

October 2011

18 pages

ISBN:9781450309455

DOI:10.1145/2179298

Copyright � 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 October 2011

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CSIIRW '11: Cyber Security and Information Intelligence Research Workshop

October 12 - 14, 2011

Tennessee, Oak Ridge, USA

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Cited By

Aiyanyo ISamuel HLim H(2020)A Systematic Review of Defensive and Offensive Cybersecurity with Machine LearningApplied Sciences10.3390/app1017581110:17(5811)Online publication date: 22-Aug-2020
https://doi.org/10.3390/app10175811
Saeed ISelamat ARohani MKrejcar OChaudhry J(2020)A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion DetectionIEEE Access10.1109/ACCESS.2020.30274638(180184-180209)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3027463
Zincir-Heywood NKayacik G(2017)Evolutionary computation in network management and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067726(1094-1112)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3067726
Rahman MOo AMahmud MPota H(2016)A multi-agent approach for security of future power grid protection systems2016 IEEE Power and Energy Society General Meeting (PESGM)10.1109/PESGM.2016.7741880(1-5)Online publication date: Jul-2016
https://doi.org/10.1109/PESGM.2016.7741880
Carvalho MEskridge TFerguson-Walter KPaltzer N(2015)MIRA: a support infrastructure for cyber command and control operations2015 Resilience Week (RWS)10.1109/RWEEK.2015.7287426(1-6)Online publication date: Aug-2015
https://doi.org/10.1109/RWEEK.2015.7287426
Rey GCarvalho MTrentesaux D(2013)Cooperation models between humans and artificial self-organizing systems: Motivations, issues and perspectives2013 6th International Symposium on Resilient Control Systems (ISRCS)10.1109/ISRCS.2013.6623769(156-161)Online publication date: Aug-2013
https://doi.org/10.1109/ISRCS.2013.6623769

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