Personalization　collaborative　filtering　recommender　systems　(CFRSs)　are　the　crucial　components　of　popular　E-commerce　services.　In　practice,　CFRSs　are　also　particularly　vulnerable　to　＂shilling＂　attacks　or　＂profile　injection＂　attacks　due　to　their　openness.　The　attackers　can　inject　well-designed　attack　profiles　into　CFRSs　in　order　to　bias　the　recommendation　results　to　their　benefits.　To　reduce　this　risk,　various　detection　techniques　have　been　proposed　to　detect　such　attacks,　which　use　diverse　features　extracted　from　user　profiles.　However,　relying　on　limited　features　to　improve　the　detection　performance　is　difficult　seemingly,　since　the　existing　features　can　not　fully　characterize　the　attack　profiles　and　genuine　profiles.　In　this　paper,　we　propose　a　novel　detection　method　to　make　recommender　systems　resistant　to　such　attacks.　The　existing　features　can　be　briefly　summarized　as　two　aspects　including　rating　behavior　based　and　item　distribution　based.　We　firstly　formulate　the　problem　as　finding　a　mapping　model　between　rating　behavior　and　item　distribution　by　exploiting　the　least-squares　approximate　solution.　Based　on　the　trained　model,　we　design　a　detector　by　employing　a　regressor　to　detect　such　attacks.　Extensive　experiments　on　both　the　MovieLens-100K　and　MovieLens-ml-latest-small　datasets　examine　the　effectiveness　of　the　proposed　detection　method.　Experimental　results　demonstrate　the　outperformance　of　the　proposed　approach　in　comparison　with　benchmarked　method　including　KNN.