Elements of information theory
Elements of information theory
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Computer Systems (TOCS)
Two Dimensional Time-Series for Anomaly Detection and Regulation in Adaptive Systems
DSOM '02 Proceedings of the 13th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Management Technologies for E-Commerce and E-Business Applications
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Principle Components and Importance Ranking of Distributed Anomalies
Machine Learning
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
EventRank: a framework for ranking time-varying networks
Proceedings of the 3rd international workshop on Link discovery
Probabilistic anomaly detection in distributed computer networks
Science of Computer Programming
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We discuss an approach to reducing the number of events accepted by anomaly detection systems, based on alternative schemes for interest-ranking. The basic assumption is that regular and periodic usage of a system will yield patterns of events that can be learned by data-mining. Events that deviate from this pattern can then be filtered out and receive special attention. Our approach compares the anomaly detection framework from Cfengine and the EventRank algorithm for the analysis of the event logs. We show that the EventRank algorithm can be used to successfully prune periodic events from real-life data.