Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A classification-based methodology for planning audit strategies in fraud detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of fraud rules for telecommunications—challenges and solutions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Machine Learning
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ADMIT: anomaly-based data mining for intrusions
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
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data mining for early disease outbreak detection
Data mining for early disease outbreak detection
The VLDB Journal — The International Journal on Very Large Data Bases
Duplicate detection in click streams
WWW '05 Proceedings of the 14th international conference on World Wide Web
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing)
Outlier detection by active learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining
Communal Detection of Implicit Personal Identity Streams
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
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
ODDC: outlier detection using distance distribution clustering
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Temporal representation in spike detection of sparse personal identity streams
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Neural fraud detection in credit card operations
IEEE Transactions on Neural Networks
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The aim has been to propose a fraud detection system with capabilities of minimizing false alarms. In this paper we introduce a technique, which uses a hybrid fraud scoring and spike detection technique in streaming data over time and space. The technique itself differentiates normal, fraud and anomalous links, and increases the suspicion of fraud links with a dynamic global black list. Also, it mitigates the suspicion of normal links with a dynamic global white list. In addition, this technique uses spike detection technique to highlight the sudden and sharp rises in data, which can be indicative of abuse. The purpose is to derive two accurate suspicion scores for all incoming new examples in real-time. Results on mining several thousand credit application data demonstrate that the proposed technique reduces false alarm rates while maintaining a reasonable hit rate. In addition, new insights have been observed from the relationships between examples. The proposed technique takes the advantages of anomaly detection and supervised techniques. However by employing the spike detection technique, the false alarm rate is decreased. By this novel integration of techniques, the proposed technique is able to foil fraudsters' attempts, which continuously morph their styles to avoid to be detected. The results of the experiments to demonstrate the benefits of the technique are also presented in this paper.