Collaborative Approach to Automatic Classification of Heterogeneous Information Security

The messages generated by the security devices represent the necessary data for the detection of the malicious activities in an information system. The heterogeneity of the devices and the lack of a standard for the security messages make the automatic processing of the messages difficult. The messages are short, use a very wide vocabulary and have different formats. We propose in this article the collaboration between classifiers to increase the accuracy of the classification. We apply the text categorization technics for the automatic classification of security log files messages, in categories defined by an ontology. We develop an extraction module for the message attributes to reduce the vocabulary size. Then we apply two training algorithms: the k-nearest neighbour algorithm and the naive Bayes, on two corpus of security log messages. Finally we propose to collaborate the classifiers to produce a single classifier with better accuracy.