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Anomaly detection holds great potential for detecting previously unknown attacks. In order to be effective in a practical environment, anomaly detection systems have to be capable of online learning and handling concept drift. In this paper, a new adaptive anomaly detection framework, based on the use of unsupervised evolving connectionist systems, is proposed to address these issues. It is designed to adapt to normal behavior changes while still recognizing anomalies. The evolvingdoi:10.1016/j.jnca.2005.08.005 fatcat:4woc5j3ssbat7gksaa7mqpldhe