MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Toward cost-sensitive modeling for intrusion detection and response
Journal of Computer Security
A Multiple Model Cost-Sensitive Approach for Intrusion Detection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Cross-Feature Analysis for Detecting Ad-Hoc Routing Anomalies
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Intrusion detection techniques for mobile wireless networks
Wireless Networks
Black hole attack in mobile Ad Hoc networks
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
A cooperative intrusion detection system for ad hoc networks
Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks
An Intrusion Detection Tool for AODV-Based Ad hoc Wireless Networks
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Secure Routing and Intrusion Detection in Ad Hoc Networks
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Distributed Intrusion Detection for Mobile Ad Hoc Networks
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Host-based network monitoring tools for MANETs
Proceedings of the 3rd ACM international workshop on Performance evaluation of wireless ad hoc, sensor and ubiquitous networks
Rescue information system for earthquake disasters based on MANET emergency communication platform
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
A Framework for Cost Sensitive Assessment of Intrusion Response Selection
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 01
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A distributed sinkhole detection method using cluster analysis
Expert Systems with Applications: An International Journal
Performance analysis of machine learning algorithms for intrusion detection in MANETs
International Journal of Wireless and Mobile Computing
An intrusion detection & adaptive response mechanism for MANETs
Ad Hoc Networks
A novel intrusion detection system based on feature generation with visualization strategy
Expert Systems with Applications: An International Journal
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Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised classification algorithms for intrusion detection on a number of metrics. We measure their performance on a dataset, described in this paper, which includes varied traffic conditions and mobility patterns for multiple attacks. One of our goals is to investigate how classification performance depends on the problem cost matrix. Consequently, we examine how the use of uniform versusweighted cost matrices affects classifier performance. A second goal is to examine techniques for tuning classifiers when unknown attack subtypes are expected during testing. Frequently, when classifiers are tuned using cross-validation, data from the same types of attacks are available in all folds. This differs from real-world employment where unknown types of attacks may be present. Consequently, we develop a sequential cross-validation procedure so that not all types of attacks will necessarily be present across all folds, in the hope that this would make the tuning of classifiers more robust. Our results indicate that weighted cost matrices can be used effectively with most statistical classifiers and that sequential cross-validation can have a small, but significant effect for certain types of classifiers.