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Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it still suffers from detecting an unknown attack, i.e., 0-day attack, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack. Unlike the existing approaches that investigate raw traffic data, we introduced a feature extraction method in order to detect such an attack from IDS alerts [J. Song et al., 2007]. However, there is a problem that it can be only applied to limited IDS products. In this paper, we present a generalized version of the feature extraction method. To this end, we define new 7 features using only the basic 6 features of IDS alerts; detection time, source address and port, destination address and port, and signature name. In order to detect 0-day attack from IDS alerts with new 7 features, we apply an unsupervised learning technique, One-class SVM, to them. We evaluated our method over the log data of IDS that is deployed in Kyoto University, and our experimental results show that it has capability to detect not only a type of 0-day attack detected in our previous study, but also several different types of 0-day attack.