Temporal representation in spike detection of sparse personal identity streams

Authors:
Clifton Phua;Vincent Lee;Ross Gayler;Kate Smith
Affiliations:
Clayton School of Information Technology, Monash University, Melbourne;Clayton School of Information Technology, Monash University, Melbourne;Baycorp Advantage, Melbourne;Clayton School of Information Technology, Monash University, Melbourne
Venue:
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Year:
2006

Citing 8
Cited 3

Activity monitoring: noticing interesting changes in behavior

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Control chart tests based on geometric moving averages

Technometrics
Bursty and hierarchical structure in streams

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
STREAM: the stanford stream data manager (demonstration description)

Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Data mining in metric space: an empirical analysis of supervised learning performance criteria

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining for early disease outbreak detection

Data mining for early disease outbreak detection
Retrospective on Aurora

The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic data generation for deduplication and data linkage

IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning

Adaptive communal detection in search of adversarial identity crime

Proceedings of the 2007 international workshop on Domain driven data mining
Adaptive spike detection for resilient data stream mining

AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
A hybrid fraud scoring and spike detection technique in streaming data

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Identity crime has increased enormously over the recent years. Spike detection is important because it highlights sudden and sharp rises in intensity relative to the current identity attribute value (which can be indicative of abuse). This paper proposes the new spike analysis framework for monitoring sparse personal identity streams. For each identity example, it detects spikes in single attribute values and integrates multiple spikes from different attributes to produce a numeric suspicion score. Although only temporal representation is examined here, experimental results on synthetic and real credit applications reveal some conditions on which the framework will perform well.