A Methodology for Testing Intrusion Detection Systems
IEEE Transactions on Software Engineering
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
The 1998 Lincoln Laboratory IDS Evaluation
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Benchmarking Anomaly-Based Detection Systems
DSN '00 Proceedings of the 2000 International Conference on Dependable Systems and Networks (formerly FTCS-30 and DCCA-8)
Strategy-based behavioural biometrics: a novel approach to automated identification
International Journal of Computer Applications in Technology
3LSPG: forensic tool evaluation by three layer stochastic process-based generation of data
IWCF'10 Proceedings of the 4th international conference on Computational forensics
Towards an artificial immune system for online fraud detection
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Fraud detection for voice over IP services on next-generation networks
WISTP'10 Proceedings of the 4th IFIP WG 11.2 international conference on Information Security Theory and Practices: security and Privacy of Pervasive Systems and Smart Devices
Fraud detection in web transactions
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
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In many cases synthetic data is more suitable than authentic data for the testing and training of fraud detection systems. At the same time synthetic data suffers from some drawbacks originating from the fact that it is indeed synthetic and may not have the realism of authentic data. In order to counter this disadvantage, we have developed a method for generating synthetic data that is derived from authentic data. We identify the important characteristics of authentic data and the frauds we want to detect and generate synthetic data with these properties.