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As recent Internet threats are evolving more rapidly than ever before, one of the major challenges in designing an intrusion detection system is to provide early and accurate detection of emerging threats. In this study, a novel framework is developed for fully unsupervised training and online anomaly detection. The framework is designed so that an initial model is constructed and then it gradually evolves according to the current state of online data without any human intervention. In thedoi:10.1016/j.eswa.2011.05.058 fatcat:ygiftoenv5h7hl5scvhx4utlqq