statistical attack detection
It has been shown in recent years that effective profile injection or shilling attacks can be mounted on standard recommendation algorithms. These attacks consist of the insertion of bogus user profiles into the system database in order to manipulate the recommendation output, for example to promote or demote the predicted ratings for a particular product. A number of attack models have been proposed and some detection strategies to identify these attacks have been empirically evaluated. In this paper we show that the standard attack models can be readily detected using statistical detection techniques. We argue that insufficient consideration of the effectiveness of attacks under a constraint of statistical invariance has been taken in past research. In fact, it is possible to create effective attacks that are undetectable using the detection strategies proposed to date, including the PCA-based clustering strategy which has shown excellent performance against standard attacks. Nevertheless, these more advanced attacks can also be detected with careful design of a statistical detector. The question posed for future research is whether attack models that produce effective attack profiles that are statistically identical to genuine profiles are really possible.
detection robustness collaborative filtering recommender systems