Online Feature Ranking for Intrusion Detection Systems
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Many current approaches to the design of intrusion detec- tion systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental learning of support vector machines in order to rank the features in real time within a streaming model for network data. Some illustrative numerical experiments with two popular benchmark datasets show that our approach al- lows to adapt to the changes in normal network behaviour and novel attack patterns which have not been experienced before.
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