Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Application of SVM and ANN for intrusion detection
Computers and Operations Research
Malware: Fighting Malicious Code
Malware: Fighting Malicious Code
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Traffic flooding attack detection with SNMP MIB using SVM
Computer Communications
Intelligence system approach for computer network security
AsiaCSN '07 Proceedings of the Fourth IASTED Asian Conference on Communication Systems and Networks
Feature selection using rough-DPSO in anomaly intrusion detection
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
A fusion of ICA and SVM for detection computer attacks
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Proceedings of the Fifth International Conference on Security of Information and Networks
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In this paper, we propose a new intrusion detection system: MMIDS (Multi-step Multi-class Intrusion Detection System), which alleviates some drawbacks associated with misuse detection and anomaly detection. The MMIDS consists of a hierarchical structure of one-class SVM, novel multi-class SVM, and incremental clustering algorithm: Fuzzy-ART. It is able to detect novel attacks, to give detail informations of attack types, to provide economic system maintenance, and to provide incremental update and extension with a system.