An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Text classification using string kernels
The Journal of Machine Learning Research
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
User identification via process profiling: extended abstract
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
Enforcing security with behavioral fingerprinting
Proceedings of the 7th International Conference on Network and Services Management
Masquerader classification system with linux command sequences using machine learning algorithms
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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Masqueraders, despite widespread use of security products such as firewalls and intrusion detection systems, are serious threats to organizations. Although anomaly detection techniques have been considered as an effective approach to complement existing security solutions, they are not widely used in practice due to poor accuracy and relatively high degree of false alarms. In this paper, we performed an empirical study investigating the effectiveness of SVM and sequence-based kernel methods. Sequence-based kernel methods showed slightly better performance than generic RBF kernel with same frequency of false alarms. In addition, the composition of two kernel methods showed that frequency of false alarms could be further reduced.