Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
DEMIDS: a misuse detection system for database systems
Integrity and internal control information systems
Soft Computing and Fuzzy Logic
IEEE Software
Learning Fingerprints for a Database Intrusion Detection System
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
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 Computer Host-Based User Anomaly Detection System Using the Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
A data mining approach for database intrusion detection
Proceedings of the 2004 ACM symposium on Applied computing
Intrusion Detection in RBAC-administered Databases
ACSAC '05 Proceedings of the 21st Annual Computer Security Applications Conference
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Proceedings of the 2nd SIGMOD PhD workshop on Innovative database research
Weighted intra-transactional rule mining for database intrusion detection
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Application of fuzzy logic for distributed intrusion detection
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Two-Stage credit card fraud detection using sequence alignment
ICISS'06 Proceedings of the Second international conference on Information Systems Security
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a novel approach for detecting intrusions in databases based on fuzzy logic, which combines evidences from user's current as well as past behavior. A first-order Sugeno fuzzy model is used to compute an initial belief for each transaction. Whether the current transaction is genuine, suspicious or intrusive is first decided based on this belief. If a transaction is found to be suspicious, its posterior belief is computed using the previous suspicion score and the fuzzy evidences obtained from the history databases by applying fuzzy-Bayesian inferencing. Final decision is made about a transaction according to its current suspicion score. Evaluation of the proposed method clearly shows that the application of fuzzy logic significantly reduces the number of false alarms, which is one of the core problems of existing database intrusion detection systems.