Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Beyond Fear: Thinking Sensibly about Security in an Uncertain World
Beyond Fear: Thinking Sensibly about Security in an Uncertain World
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Communal Detection of Implicit Personal Identity Streams
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Adaptive communal detection in search of adversarial identity crime
Proceedings of the 2007 international workshop on Domain driven 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
Towards fraud detection support using grid technology
Multiagent and Grid Systems - New tendencies on agents and grid environments
Adaptive methods for classification in arbitrarily imbalanced and drifting data streams
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
A hybrid fraud scoring and spike detection technique in streaming data
Intelligent Data Analysis
Discovery and diagnosis of behavioral transitions in patient event streams
ACM Transactions on Management Information Systems (TMIS)
| Hi-index | 0.00 |
Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering some attributes with extreme weights to choose the best ones for computing each example's suspicion score. Within an identity crime detection domain, adaptive spike detection is validated on a few million real credit applications with adversarial activity. The results are F-measure curves on eleven experiments and relative weights discussion on the best experiment. The results reinforce adaptive spike detection's effectiveness for class-label-free attribute ranking and selection.