Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The base-rate fallacy and the difficulty of intrusion detection
ACM Transactions on Information and System Security (TISSEC)
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
Principles of Computer Security: Security+ and Beyond
Principles of Computer Security: Security+ and Beyond
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Hybrid flexible neural-tree-based intrusion detection systems: Research Articles
International Journal of Intelligent Systems
A hierarchical SOM-based intrusion detection system
Engineering Applications of Artificial Intelligence
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
A new intrusion detection system using support vector machines and hierarchical clustering
The VLDB Journal — The International Journal on Very Large Data Bases
A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
A Probabilistic Approach for Network Intrusion Detection
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Information Sciences: an International Journal
A Feature Selection Approach for Network Intrusion Detection
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Expert Systems with Applications: An International Journal
Layered Approach Using Conditional Random Fields for Intrusion Detection
IEEE Transactions on Dependable and Secure Computing
Comparing Single and Multiple Bayesian Classifiers Approaches for Network Intrusion Detection
ICCEA '10 Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 02
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
Applied Intelligence
On the effect of calibration in classifier combination
Applied Intelligence
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
Ensemble canonical correlation analysis
Applied Intelligence
Boosting-SVM: effective learning with reduced data dimension
Applied Intelligence
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Network intrusion detection research work that employed KDDCup 99 dataset often encounter challenges in creating classifiers that could handle unequal distributed attack categories. The accuracy of a classification model could be jeopardized if the distribution of attack categories in a training dataset is heavily imbalanced where the rare categories are less than 2% of the total population. In such cases, the model could not efficiently learn the characteristics of rare categories and this will result in poor detection rates. In this research, we introduce an efficient and effective approach in dealing with the unequal distribution of attack categories. Our approach relies on the training of cascaded classifiers using a dichotomized training dataset in each cascading stage. The training dataset is dichotomized based on the rare and non-rare attack categories. The empirical findings support our arguments that training cascaded classifiers using the dichotomized dataset provides higher detection rates on the rare categories as well as comparably higher detection rates for the non-rare attack categories as compared to the findings reported in other research works. The higher detection rates are due to the mitigation of the influence from the dominant categories if the rare attack categories are separated from the dataset.