The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Artificial Intelligence Review
Combining heterogeneous classifiers for network intrusion detection
ASIAN'07 Proceedings of the 12th Asian computing science conference on Advances in computer science: computer and network security
A New Weighted Ensemble Model for Detecting DoS Attack Streams
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Anomaly Based Network Intrusion Detection Systems (ANIDS) mechanisms are largely based on machine learning algorithms and have been found effective in detecting known as well as novel attacks. However, often these algorithms in isolation cannot accurately detect all kinds of attacks and generate lot of false alarms. In this paper, we intend to show that if the power of each of the algorithms are combined and harnessed using an appropriate ensemble method, a significant improvement in detection rate can be achieved. The performance of our meta ensemble classifier was evaluated over several real life intrusion datasets and the benchmark KDD'99 dataset, and the results have been found excellent in comparison to its other competing algorithms.