C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Classification of Fault-Prone Software Modules: Prior Probabilities,Costs, and Model Evaluation
Empirical Software Engineering
Evaluating indirect and direct classification techniques for network intrusion detection
Intelligent Data Analysis
Indirect classification approaches: a comparative study in network intrusion detection
International Journal of Computer Applications in Technology
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
An intrusion detection technique based on continuous binary communication channels
International Journal of Security and Networks
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Network security has become an important issue in today's extensively interconnected computer world. The industry, academic institutions, small and large businesses and even residences have never been more at risk from the increasing onslaught of computer attacks than more recently. Such malicious efforts cause damage ranging from mere violation of confidentiality and issues of privacy up to actual financial losses if business operations are compromised. Intrusion detection systems (IDS) have been used along with data mining and machine learning efforts to detect intruders. However, with the limitation of organizational resources, it is unreasonable to inspect every network alarm raised by the IDS. Towards resource-and cost-sensitive IDS models, we investigate the Modified Expected Cost of Misclassification as a model selection measure for building a goal oriented intrusion detection classifier. The case study presented is that of the DARPA 1998 offline intrusion detection project. The empirical results show a promise for building a resource-based intrusion detection model.