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Visualizing graph features for fast port scan detection

Published: 08 January 2013 Publication History

Abstract

Detection of sophisticated network scans, such as low and slow scans, requires correlation of large amounts of network data over long periods of time. The volume of data obfuscating such scans can be overwhelming and makes computation challenging. Such scans pose network security risks since identifying running services, the goal of executing such scans, is the first step in launching an attack on the scanned host. To detect sophisticated scans we propose the integration of graph feature extraction techniques with visualization to simultaneously optimize computational complexity and human analyst time. The integrated approach uses graph modeling and preprocessing to make visual displays easy to comprehend, and uses human intervention to avoid solving NP-hard computational problems while still providing real-time visualization.

References

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R. F. Erbacher and K. A. Forcht, "Combining visualization and interaction for scalable detection of anomalies in network data," The Journal of Computer Information Systems, Vol. 50, No. 4, pp. 117--126, 2010.
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J. P. Anderson, "Computer Security Threat Monitoring and Surveillance," James P. Anderson Co., Fort Washington, PA (Apr. 1980).
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D. E. Denning, "An Intrusion-Detection Model," IEEE Trans. on Software Eng., Vol. 13, No. 2, Feb. 1987, pp. 222--232; also in Proc. of the 1986 Symp. on Security and Privacy, IEEE Computer Society, April 1986, pp. 118--131.
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B. Shah and B. H. Trivedi, "Artificial Neural Network based Intrusion Detection System: A Survey," International Journal of Computer Applications, Vol. 39, No. 6, pp. 13--18, 2012.
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S. Axelsson, "The base-rate fallacy and the difficulty of intrusion detection," ACM Trans. Inf. Syst. Secur., Vol. 3, No. 3, pp. 186--205, August 2010.
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Zhang Jiawan, Li Liang, Lu Liangfu and Zhou Ning, "A Novel Visualization Approach for Efficient Network Scans Detection", International Conference on Security Technology (SECTECH), 2008.
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Muelder, C., Kwan-Liu Ma and Bartoletti, T., "A Visualization Methodology for Characterization of Network Scans", IEEE Workshop on Visualization for Computer Security (VizSEC), 2005.

Cited By

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  • (2022)Predicting the Compressive Strength of Alkali-Activated Concrete Using Various Data Mining MethodsProceedings of the Canadian Society of Civil Engineering Annual Conference 202110.1007/978-981-19-1004-3_26(317-326)Online publication date: 24-May-2022

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cover image ACM Other conferences
CSIIRW '13: Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop
January 2013
282 pages
ISBN:9781450316873
DOI:10.1145/2459976

Sponsors

  • Los Alamos National Labs: Los Alamos National Labs
  • Sandia National Labs: Sandia National Laboratories
  • DOE: Department of Energy
  • Oak Ridge National Laboratory
  • Lawrence Livermore National Lab.: Lawrence Livermore National Laboratory
  • BERKELEYLAB: Lawrence National Berkeley Laboratory
  • Argonne Natl Lab: Argonne National Lab
  • Idaho National Lab.: Idaho National Laboratory
  • Pacific Northwest National Laboratory
  • Nevada National Security Site: Nevada National Security Site

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2013

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  • Research-article

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CSIIRW '13
Sponsor:
  • Los Alamos National Labs
  • Sandia National Labs
  • DOE
  • Lawrence Livermore National Lab.
  • BERKELEYLAB
  • Argonne Natl Lab
  • Idaho National Lab.
  • Nevada National Security Site
CSIIRW '13: Cyber Security and Information Intelligence
January 8 - 10, 2013
Tennessee, Oak Ridge, USA

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View all
  • (2022)Predicting the Compressive Strength of Alkali-Activated Concrete Using Various Data Mining MethodsProceedings of the Canadian Society of Civil Engineering Annual Conference 202110.1007/978-981-19-1004-3_26(317-326)Online publication date: 24-May-2022

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