The Strength of Weak Learnability
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
A clustering-based method for unsupervised intrusion detections
Pattern Recognition Letters
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
An efficient intrusion detection system using a boosting-based learning algorithm
International Journal of Computer Applications in Technology
Ensemble of One-Class Classifiers for Network Intrusion Detection System
IAS '08 Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security
McPAD: A multiple classifier system for accurate payload-based anomaly detection
Computer Networks: The International Journal of Computer and Telecommunications Networking
An adaptive genetic-based signature learning system for intrusion detection
Expert Systems with Applications: An International Journal
Network intrusion detection by combining one-class classifiers
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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This study introduces a new method based on Greedy-Boost, a multiple classifier system, for better and faster intrusion detection. Detection of the anomalies in the data-processing networks is regarded as a problem of data classification allowing to use data mining and machine learning techniques to perform intrusion detection. With such automatic processing procedures, human expertise only focuses on a small set of potential anomalies which may result in important time savings and efficiency. In order to be scalable and efficient, these kinds of approaches must respect important requirements. The first is to obtain a high level of precision, that is to be able to detect a maximum of anomalies with a minimum of false alarms. The second is to detect potential anomalies as fast as possible. We propose Greedy-Boost, a new approach of boosting which is based on an adaptive combination of multiple classifiers to perform the precision of the detection. This approach uses an aspect of smooth that ensures stability of the classifier system and offers speed of detection. The experimental results, conducted on the KDD99 dataset, prove that our proposed approach outperforms several state-of-the-art methods, particularly in detecting rare attack types.