The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
DEMIDS: a misuse detection system for database systems
Integrity and internal control information systems
Intrusion detection
Data mining: concepts and techniques
Data mining: concepts and techniques
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Learning Fingerprints for a Database Intrusion Detection System
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
Mining intrusion detection alarms for actionable knowledge
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Intrusion Detection in Real-Time Database Systems via Time Signatures
RTAS '00 Proceedings of the Sixth IEEE Real Time Technology and Applications Symposium (RTAS 2000)
A data mining approach for database intrusion detection
Proceedings of the 2004 ACM symposium on Applied computing
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Detection of Database Intrusion Using a Two-Stage Fuzzy System
ISC '09 Proceedings of the 12th International Conference on Information Security
Process mining and security: visualization in database intrusion detection
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
Two-stage database intrusion detection by combining multiple evidence and belief update
Information Systems Frontiers
Self-protecting and self-optimizing database systems: implementation and experimental evaluation
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
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Data mining is the non-trivial process of identifying novel, potentially useful and understandable patterns in data. With most of the organizations starting on-line operations, the threat of security breaches is increasing. Since a database stores a lot of valuable information, its security has become paramount. One mechanism to safeguard the information in these databases is to use an intrusion detection system(IDS). In every database, there are a few attributes or columns that are more important to be tracked or sensed for malicious modifications as compared to the other attributes. In this paper, we propose an intrusion detection algorithm named weighted data dependency rule miner (WDDRM) for finding dependencies among the data items. The transactions that do not follow the extracted data dependency rules are marked as malicious. We show that WDDRM handles the modification of sensitive attributes quite accurately.