Classification and detection of computer intrusions
Classification and detection of computer intrusions
Practical Intrusion Detection Handbook
Practical Intrusion Detection Handbook
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Connection scheduling in web servers
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Mining based decision support multi-agent system for personalized e-healthcare service
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
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For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, precise normal pattern is necessary. For this, the understanding of the characteristics of data on network is inevitable. In this paper we propose to use clustering and association rules as the basis for guiding anomaly detection in mobile environment. We present dynamic transaction for generating more effectively detection patterns. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data.