The defacement of web sites has become a widespread problem. Reaction to these incidents is often quite slow and triggered by occasional checks or even feedback from users, because organizations usually lack a systematic and round the clock surveillance of the integrity of their web sites. A more systematic approach is certainly desirable. An attractive option in this respect consists in augmenting availability and performance monitoring services with defacement detection capabilities. Motivated by these considerations, in this paper we assess the performance of several anomaly detection approaches when faced with the problem of detecting web defacements automatically. All these approaches
construct a profile of the monitored page automatically, based on machine learning techniques, and raise an alert when the page content does not fit the profile. We assessed their performance in terms of false positives and false negatives on a dataset composed of 300 highly dynamic web pages that we observed for three months and includes a set of 320 real defacements.
