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Discovering collaborative cyber attack patterns using social network analysis

*Corresponding author for this work
  • Rochester Institute of Technology
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution

Abstract

This paper investigates collaborative cyber attacks based on social network analysis. An Attack Social Graph (ASG) is defined to represent cyber attacks on the Internet. Features are extracted from ASGs to analyze collaborative patterns. We use principle component analysis to reduce the feature space, and hierarchical clustering to group attack sources that exhibit similar behavior. Experiments with real world data illustrate that our framework can effectively reduce from large dataset to clusters of attack sources exhibiting critical collaborative patterns.

Bibliographic Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution

Original language

English

Pages from-to (Number of pages)

Pages 129-136 (8 pages)

Publication milestones

  • Published - 2011

Publication status

Published - 2011

Publication series

  • Publication series name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print): 0302-9743
    ISSN (Electronic): 1611-3349
    Volume: 6589 LNCS
9783642196553

Publication IDs

  • Scopus: 79952396643

Host publication title

Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings