IEEE Transactions on Software Engineering - Special issue on computer security and privacy
The nature of statistical learning theory
The nature of statistical learning theory
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
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
ACM Transactions on Information and System Security (TISSEC)
Machine Learning
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Local Dimensionality Reduction for Locally Weighted Learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Discriminative parameter learning for Bayesian networks
Proceedings of the 25th international conference on Machine learning
Application of Data Mining to Network Intrusion Detection: Classifier Selection Model
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Network Intrusion Detection and Prevention: Concepts and Techniques
Network Intrusion Detection and Prevention: Concepts and Techniques
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Finite mixture model of bounded semi-naive Bayesian networks classifier
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering
Expert Systems with Applications: An International Journal
Toward credible evaluation of anomaly-based intrusion-detection methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Random-Forests-Based Network Intrusion Detection Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Intrusion detection systems (IDSs) are currently drawing a great amount of interest as a key part of system defence. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Recently, machine learning methodologies are playing an important role in detecting network intrusions (or attacks), which further helps the network administrator to take precautionary measures for preventing intrusions. In this paper, we propose to use ten machine learning approaches that include Decision Tree (J48), Bayesian Belief Network, Hybrid Naive Bayes with Decision Tree, Rotation Forest, Hybrid J48 with Lazy Locally weighted learning, Discriminative multinomial Naive Bayes, Combining random Forest with Naive Bayes and finally ensemble of classifiers using J48 and NB with AdaBoost (AB) to detect network intrusions efficiently. We use NSL-KDD dataset, a variant of widely used KDDCup 1999 intrusion detection benchmark dataset, for evaluating our proposed machine learning approaches for network intrusion detection. Finally, Experimental results with 5-class classification are demonstrated that include: Detection rate, false positive rate, and average cost for misclassification. These are used to aid a better understanding for the researchers in the domain of network intrusion detection.