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Intrusion detection is an active research field in the development of reliable network, where many machine learning techniques are exploited to fit the specific application. Although some detection algorithms have been developed, they lack the adaptability to the frequently changing network environments, since they are mostly trained in batch mode. In this paper, the authors propose an online boosting based intrusion detection method, which has the ability of efficient online learning of new network intrusions. The detection can be performed in real-time with high detection accuracy. Experimental results show its advantage in the intrusion detection application.
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