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In this paper, we introduce a new machine-learning-based data classification algorithm that is applied
to network intrusion detection. The basic task is to classify network activities (in the network log
as connection records) as normal or abnormal while minimizing misclassification. Although different
classification models have been developed for network intrusion detection, each of them has its strengths
and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the
Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM
method with CSOACNs to take the advantages of both while avoiding their we
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