A model based reasoning approach for generating plausible crime scenarios from evidence
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
A Case-Based Approach to Anomaly Intrusion Detection
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case-oriented alert correlation
WSEAS Transactions on Computers
Cooperative Intrusion Detection Model Based on State Transition Analysis
Computer Supported Cooperative Work in Design IV
WSEAS Transactions on Information Science and Applications
Using AI for e-government automatic assessment of immigration application forms
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Knowledge based crime scenario modelling
Expert Systems with Applications: An International Journal
A neural network model for detection systems based on data mining and false errors
EUC'06 Proceedings of the 2006 international conference on Emerging Directions in Embedded and Ubiquitous Computing
Situational awareness through reasoning on network incidents
Proceedings of the 4th ACM conference on Data and application security and privacy
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Recently there has been significant interest in applying artificial intelligence (AI) techniques to the intrusion detection problem. Attempts have been made to develop rule based and model based expert systems for intrusion detection. Although these systems have been useful for detecting intruders, they face difficulties in acquiring and representing the knowledge. We present and describe a case based reasoning approach to intrusion detection which alleviates some of the difficulties of current approaches.