Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Network Intrusion Detection: An Analyst's Handbook
Network Intrusion Detection: An Analyst's Handbook
Cryptography and Network Security: Principles and Practice
Cryptography and Network Security: Principles and Practice
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Application of SVM and ANN for intrusion detection
Computers and Operations Research
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
A latent class modeling approach to detect network intrusion
Computer Communications
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
Semi-supervised single-label text categorization using centroid-based classifiers
Proceedings of the 2007 ACM symposium on Applied computing
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
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
Alert correlation in collaborative intelligent intrusion detection systems-A survey
Applied Soft Computing
Expert Systems with Applications: An International Journal
ICICS'11 Proceedings of the 13th international conference on Information and communications security
An effective unsupervised network anomaly detection method
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Intelligent intrusion detection system using fuzzy rough set based C4.5 algorithm
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
A real time anomaly detection system based on probabilistic artificial immune based algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Evaluation on multivariate correlation analysis based denial-of-service attack detection system
Proceedings of the First International Conference on Security of Internet of Things
IDS false alarm reduction using an instance selection KNN-memetic algorithm
International Journal of Metaheuristics
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
A novel intrusion detection system based on feature generation with visualization strategy
Expert Systems with Applications: An International Journal
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Intrusion detection is a necessary step to identify unusual access or attacks to secure internal networks. In general, intrusion detection can be approached by machine learning techniques. In literature, advanced techniques by hybrid learning or ensemble methods have been considered, and related work has shown that they are superior to the models using single machine learning techniques. This paper proposes a hybrid learning model based on the triangle area based nearest neighbors (TANN) in order to detect attacks more effectively. In TANN, the k-means clustering is firstly used to obtain cluster centers corresponding to the attack classes, respectively. Then, the triangle area by two cluster centers with one data from the given dataset is calculated and formed a new feature signature of the data. Finally, the k-NN classifier is used to classify similar attacks based on the new feature represented by triangle areas. By using KDD-Cup '99 as the simulation dataset, the experimental results show that TANN can effectively detect intrusion attacks and provide higher accuracy and detection rates, and the lower false alarm rate than three baseline models based on support vector machines, k-NN, and the hybrid centroid-based classification model by combining k-means and k-NN.