IEEE Transactions on Software Engineering - Special issue on computer security and privacy
ACM Computing Surveys (CSUR)
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Squeezer: an efficient algorithm for clustering categorical data
Journal of Computer Science and Technology
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Clustering and classification algorithm for computer intrusion detection
Clustering and classification algorithm for computer intrusion detection
LDBOD: A novel local distribution based outlier detector
Pattern Recognition Letters
Locally linear reconstruction for instance-based learning
Pattern Recognition
Review: Intrusion detection by machine learning: A review
Expert Systems with Applications: An International Journal
A Combination Classification Algorithm Based on Outlier Detection and C4.5
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
An Outlier Detection Algorithm Based on Arbitrary Shape Clustering
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
An approach to model building for accelerated cooling process using instance-based learning
Expert Systems with Applications: An International Journal
Outlier detection techniques for process mining applications
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Framework of clustering-based outlier detection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Approach based ensemble methods for better and faster intrusion detection
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
An improved K-nearest-neighbor algorithm for text categorization
Expert Systems with Applications: An International Journal
A differentiated one-class classification method with applications to intrusion detection
Expert Systems with Applications: An International Journal
An unsupervised feature selection framework based on clustering
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
An effective unsupervised network anomaly detection method
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
An evaluation of clustering technique over intrusion detection system
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Advanced probabilistic approach for network intrusion forecasting and detection
Expert Systems with Applications: An International Journal
A generalized cluster centroid based classifier for text categorization
Information Processing and Management: an International Journal
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Detection of intrusion attacks is an important issue in network security. This paper considers the outlier factor of clusters for measuring the deviation degree of a cluster. A novel method is proposed to compute the cluster radius threshold. The data classification is performed by an improved nearest neighbor (INN) method. A powerful clustering-based method is presented for the unsupervised intrusion detection (CBUID). The time complexity of CBUID is linear with the size of dataset and the number of attributes. The experiments demonstrate that our method outperforms the existing methods in terms of accuracy and detecting unknown intrusions.