Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Relational Data Mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
ACM SIGMOD Record
Proceedings of the 2008 ACM symposium on Applied computing
Discovering Relational Emerging Patterns
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Discovering emerging patterns for anomaly detection in network connection data
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Visualization of similarities and dissimilarities in rules using multidimensional scaling
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Learning the daily model of network traffic
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previous solutions by considering the case of objects modeled by multiple database relations. Frequent relational patterns are efficiently extracted at each time point, and a time window is used to filter out novelty patterns. An application of the proposed algorithm to the problem of detecting anomalies in network traffic is described and quantitative and qualitative results obtained by analyzing real stream of data collected from the firewall logs are reported.