A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Snort 2.0 Intrusion Detection
Text classification using string kernels
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Principles of Information Security
Principles of Information Security
An empirical study of three machine learning methods for spam filtering
Knowledge-Based Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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For network intrusion and virus detection, ordinary methods detect malicious network traffic and viruses by examining packets, flow logs or content of memory for any signatures of the attack. This implies that if no signature is known/created in advance, attack detection will be problematical. Addressing unknown attacks detection, we develop in this paper a network traffic and spam analyzer using a string kernel based SVM (support vector machine) supervised machine learning. The proposed method is capable of detecting network attack without known/earlier determined attack signatures, as SVM automatically learning attack signatures from traffic data. For application to internet security, we have implemented the proposed method for spam email detection over the SpamAssasin and E. M. Canada datasets, and network application authentication via real connection data analysis. The obtained above 99% accuracies have demonstrated the usefulness of string kernel SVMs on network security for either detecting `abnormal' or protecting `normal' traffic.