Computing the minimum Hausdorff distance between two point sets on a line under translation
Information Processing Letters
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Building a Chemical Ontology Using Methontology and the Ontology Design Environment
IEEE Intelligent Systems
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Outlier Detection Integrating Semantic Knowledge
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
Automatically detecting deceptive criminal identities
Communications of the ACM - Homeland security
Untangling criminal networks: a case study
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Agency interoperation for effective data mining in border control and homeland security applications
dg.o '05 Proceedings of the 2005 national conference on Digital government research
Semantics-based threat structure mining
dg.o '06 Proceedings of the 2006 international conference on Digital government research
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The ability to identify collusive malicious behavior is critical in today's security environment. We pose the general problem of Collusion Set Detection (CSD): identifying sets of behavior that together satisfy some notion of “interesting behavior”. For this paper, we focus on a subset of the problem (called CSD′), by restricting our attention only to outliers. In the process of proposing the solution, we make the following novel research contributions: First, we propose a suitable distance metric, called the collusion distance metric, and formally prove that it indeed is a distance metric. We propose a collusion distance based outlier detection (CDB) algorithm that is capable of identifying the causal dimensions (n) responsible for the outlierness, and demonstrate that it improves both precision and recall, when compared to the Euclidean based outlier detection. Second, we propose a solution to the CSD′ problem, which relies on the semantic relationships among the causal dimensions.