Clustering toward detecting cyber attacks

Several anomaly methods have been proposed to cope with the recent booming of HTTP-related vulnerabilities which renders the security breaches of lots of vital HTTP-based services on the internet. This paper proposes a novel bottom-up agglomerative clustering method which not only spares the nuisance of a learning process that involves a big amount of manual sample taggings, but also presents a much stronger adaptiveness in being able to coping with variant situations and in detecting new samples.