Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Genetic Programming Based WiFi Data Link Layer Attack Detection
CNSR '06 Proceedings of the 4th Annual Communication Networks and Services Research Conference
A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Your 80211 wireless network has no clothes
IEEE Wireless Communications
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
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We address the problem of evaluating the robustness of machine learning based detectors for deployment in real life networks. To this end, we employ Genetic Programming for evolving classifiers and Artificial Neural Networks as our machine learning paradigms under three different Denial-of-Service attacks at the Data Link layer De-authentication, Authentication and Association attacks. We investigate their cross-platform robustness and cross-attack robustness. Cross-platform robustness is the ability to seamlessly port an Intrusion Detector trained on one network to another network with little or no change and without a drop in performance. Cross-attack robustness is the ability of a detector trained on one attack type to detect a different but similar attack on which it has not been trained. Our results show that the potential of a machine learning based detector can be significantly enhanced or limited by the representation of the training data for the learning algorithms.