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
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Neural Networks for Intelligent Signal Processing (Series on Innovative Intelligence, Vol. 4)
Neural Networks for Intelligent Signal Processing (Series on Innovative Intelligence, Vol. 4)
Characterizing Spam Traffic and Spammers
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Design and Evaluation of a Bayesian-filter-based Image Spam Filtering Method
ISA '08 Proceedings of the 2008 International Conference on Information Security and Assurance (isa 2008)
Anomaly Detection Using LibSVM Training Tools
ISA '08 Proceedings of the 2008 International Conference on Information Security and Assurance (isa 2008)
Z-AdaBoost: Boosting 2-Thresholded Weak Classifiers for Object Detection
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 02
Improved Bayesian Anti-Spam Filter Implementation and Analysis on Independent Spam Corpuses
ICCET '09 Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Modeling Spammer Behavior: Naïve Bayes vs. Artificial Neural Networks
ICIMT '09 Proceedings of the 2009 International Conference on Information and Multimedia Technology
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In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.