Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Learning Rules for Anomaly Detection of Hostile Network Traffic
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Support Vector Data Description
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Using Active Learning in Intrusion Detection
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Minimum Enclosing and Maximum Excluding Machine for Pattern Description and Discrimination
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
USENIX-SS'06 Proceedings of the 15th conference on USENIX Security Symposium - Volume 15
Transductive support vector machines for structured variables
Proceedings of the 24th international conference on Machine learning
Detecting unknown network attacks using language models
DIMVA'06 Proceedings of the Third international conference on Detection of Intrusions and Malware & Vulnerability Assessment
Condensed nearest neighbor data domain description
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Anagram: a content anomaly detector resistant to mimicry attack
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
Anomaly detection in IP networks
IEEE Transactions on Signal Processing
Toward supervised anomaly detection
Journal of Artificial Intelligence Research
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Data domain description techniques aim at deriving concise descriptions of objects belonging to a category of interest. For instance, the support vector domain description (SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points lying outside of the ball are considered anomalous. However, relevant information such as expert and background knowledge remain unused in the unsupervised setting. In this paper, we rephrase data domain description as a semi-supervised learning task, that is, we propose a semi-supervised generalization of data domain description (SSSVDD) to process unlabeled and labeled examples. The corresponding optimization problem is non-convex. We translate it into an unconstraint, continuous problem that can be optimized accurately by gradient-based techniques. Furthermore, we devise an effective active learning strategy to query low-confidence observations. Our empirical evaluation on network intrusion detection and object recognition tasks shows that our SSSVDDs consistently outperform baseline methods in relevant learning settings.