Collaborative　filtering　recommender　systems　(CFRSs)　are　critical　components　of　existing　popular　e-commerce　websites　to　make　personalized　recommendations.　In　practice,　CFRSs　are　highly　vulnerable　to　＂shilling＂　attacks　or　＂profile　injection＂　attacks　due　to　its　openness.　A　number　of　detection　methods　have　been　proposed　to　make　CFRSs　resistant　to　such　attacks.　However,　some　of　them　distinguished　attackers　by　using　typical　similarity　metrics,　which　are　difficult　to　fully　defend　all　attackers　and　show　high　computation　time,　although　they　can　be　effective　to　capture　the　concerned　attackers　in　some　extent.　In　this　paper,　we　propose　an　unsupervised　method　to　detect　such　attacks.　Firstly,　we　filter　out　more　genuine　users　by　using　suspected　target　items　as　far　as　possible　in　order　to　reduce　time　consumption.　Based　on　the　remained　result　of　the　first　stage,　we　employ　a　new　similarity　metric　to　further　filter　out　the　remained　genuine　users,　which　combines　the　traditional　similarity　metric　and　the　linkage　information　between　users　to　improve　the　accuracy　of　similarity　of　users.　Experimental　results　show　that　our　proposed　detection　method　is　superior　to　benchmarked　method.