Collaborative　filtering　recommender　systems　(CFRSs)　are　the　key　components　of　successful　E-commerce　systems.　However,　CFRSs　are　highly　vulnerable　to　＂shilling＂　attacks　or　＂profile　injection＂　attacks　due　to　its　openness.　Since　the　size　of　attackers　is　usually　far　smaller　than　genuine　users,　conventional　supervised　learning　based　detection　methods　could　be　too　＂dull＂　to　handle　such　imbalanced　classification.　In　this　paper,　we　improve　detection　performance　from　following　two　aspects.　Firstly,　we　extract　well-designed　features　from　user　profiles　based　on　the　statistical　properties　of　the　diverse　attack　models,　making　hard　detection　scenarios　become　easier　to　perform.　Then,　refer　to　the　general　idea　of　re-scale　Boosting　(RBoosting)　and　AdaBoost,　we　apply　a　variant　of　AdaBoost,　called　the　re-scale　AdaBoost　(RAdaBoost)　as　our　detection　method　based　on　the　extracted　features.　Finally,　a　series　of　experiments　on　the　MovieLens-100K　dataset　are　conducted　to　demonstrate　the　outperformance　of　RAdaBoost　over　other　competing　techniques　such　as　SVM,　kNN　and　AdaBoost.　(C)　2016　Elsevier　B.V.　All　rights　reserved.