Detecting anomalies in graphs with numeric labels

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 1197-1202

  Davis, Michael ^a   Liu, Weiru ^a   Miller, Paul ^a   Redpath, George ^b  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b CEM Systems   (United Kingdom)

Author keywords

access control systems; anomaly detection; graph mining; transaction graphs

Indexed keywords

ANOMALY DETECTION; DISCRETIZATION ALGORITHMS; GRAPH MINING; QUALITATIVE EVALUATIONS; STRUCTURAL DATA; TRANSACTION GRAPHS;

ACCESS CONTROL; ALGORITHMS; KNOWLEDGE MANAGEMENT; SEARCH ENGINES;

QUALITY CONTROL;

EID: 83055191198     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063749     Document Type: Conference Paper

References (14)

1. L. Akoglu, M. McGlohon, and C. Faloutsos. Oddball: Spotting anomalies in weighted graphs. In Advances in Knowledge Discovery and Data Mining. Springer, 2010.
2. R. P. Biuk-Aghai, Y.-W. Si, S. Fong, and P.-F. Yan. Security in physical environments: Algorithms and system for automated detection of suspicious activity. In Workshop on Behavior Informatics 2010. Springer, 2010.
3. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. LOF: Identifying density-based local outliers. SIGMOD Rec., 29(2):93-104, 2000.
4. D. J. Cook and L. B. Holder. Graph-based data mining. IEEE Intelligent Systems, 15:32-41, March 2000.
5. W. Eberle and L. Holder. Mining for insider threats in business transactions and processes. In Computational Int. and Data Mining, 2009, pages 163-170, March 2009.
6. W. Eberle and L. B. Holder. Discovering anomalies to multiple normative patterns in structural and numeric data. In 22nd International FLAIRS Conference, 2009.
7. Z. He, X. Xu, and S. Deng. Discovering cluster-based local outliers. Pattern Recog. Letters, 24(9-10):1641-1650, 2003.
8. C. Jiang, F. Coenen, and M. Zito. Frequent sub-graph mining on edge weighted graphs. In 12th Int. Conf. on Data Warehousing and Knowledge Discovery. Springer, 2010.
9. F. Mansman, L. Meier, and D. A. Keim. Visualization of host behavior for network security. In VIZSEC 2007, Mathematics and Visualization. Springer, 2008.
10. C. C. Noble and D. J. Cook. Graph-based anomaly detection. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2003.
11. G. Perez, I. Olmos, and J. A. Gonzalez. Subgraph isomorphism detection with support for continuous labels. In 23rd International FLAIRS Conference, 2010.
12. S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large data sets. SIGMOD Rec., 29:427-438, May 2000.
13. O. E. Romero, J. A. Gonzalez, and L. B. Holder. Handling of numeric ranges for graph-based knowledge discovery. In 23rd International FLAIRS Conference, 2010.
14. X. Yan and J. Han. gSpan: Graph-based substructure pattern mining. In 2002 IEEE International Conference on Data Mining, ICDM '02, pages 721-724. IEEE, 2002.