Defacement Detection with Passive Adversaries

A novel approach to defacement detection is proposed in this paper, addressing explicitly the possible presence of a passive adversary. Defacement detection is an important security measure for Web Sites and Applications, aimed at avoiding unwanted modifications that would result in significant reputational damage. As in many other anomaly detection contexts, the algorithm used to identify possible defacements is obtained via an Adversarial Machine Learning process. We consider an exploratory setting, where the adversary can observe the detector’s alarm-generating behaviour, with the purpose of devising and injecting defacements that will pass undetected. It is then necessary to make to learning process unpredictable, so that the adversary will be unable to replicate it and predict the classifier’s behaviour. We achieve this goal by introducing a secret key—a key that our adversary does not know. The key will influence the learning process in a number of different ways, that are precisely defined in this paper. This includes the subset of examples and features that are actually used, the time of learning and testing, as well as the learning algorithm’s hyper-parameters. This learning methodology is successfully applied in this context, by using the system with both real and artificially modified Web sites. A year-long experimentation is also described, referred to the monitoring of the new Web Site of a major manufacturing company

Incidence trends of colorectal cancer in the early 2000s in Italy. Figures from the IMPATTO study on colorectal cancer screening

We utilised the IMPATTO study's archives to describe the 2000-2008 colorectal cancer (CRC) incidence rate trends in Italy, once screening programmes based on the faecal immunochemical test were implemented in different areas. Data on CRCs diagnosed in Italy from 2000 to 2008 in subjects aged 40-79 years were collected by 23 cancer registries. Incidence rate trends were evaluated as a whole and by macro-area (North-Centre and South-Islands), presence of a screening programme, sex, ten-year age class, anatomic site, stage at diagnosis, and pattern of diagnosis (screen-detected, non-screen-detected). The annual percent change (APC) of incidence rate trends, with 95% confidence intervals (95%CI), were computed. The study included 46,857 CRCs diagnosed in subjects aged 40-79 years, of which 2,806 were screendetected. The incidence rates in the North-Centre were higher than in the South and on the Islands. During the study period, screening programmes had been implemented only in the North-Centre and had a significant effect on incidence rates, with an initial sharp increase in incidence, followed by a decrease that started in the 3rd-4th years of screening. These incidence rate trends were exclusively due to modifications in the rates of stage I cases. After screening programmes started, incidence increased in all anatomic sites, particularly in the distal colon. The differential figures introduced by the implementation of screening programmes warrant a continuous surveillance of CRC incidence and mortality trends to monitor the impact of screening at a national level