Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
E-Stream: Evolution-Based Technique for Stream Clustering
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
WIDS: a sensor-based online mining wireless intrusion detection system
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery from Data (TKDD)
DEDUCE: at the intersection of MapReduce and stream processing
Proceedings of the 13th International Conference on Extending Database Technology
A suspicious behaviour detection using a context space model for smart surveillance systems
Computer Vision and Image Understanding
Securing advanced metering infrastructure using intrusion detection system with data stream mining
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
| Hi-index | 0.00 |
In anomaly intrusion detection, how to model the normal behavior of activities performed by a user is an important issue. To extract the normal behavior as a profile, conventional data mining techniques are widely applied to a finite audit data set. However, these approaches can only model the static behavior of a user in the audit data set. This drawback can be overcome by viewing the continuous activities of a user as an audit data stream. This paper proposes a new clustering algorithm which continuously models a data stream. A set of features is used to represent the characteristics of an activity. For each feature, the clusters of feature values corresponding to activities observed so far in an audit data stream are identified by the proposed clustering algorithm for data streams. As a result, without maintaining any historical activity of a user physically, new activities of the user can be continuously reflected to the on-going result of clustering.