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Recommender systems rely on the opinions of many users to predict the preferences of potential customers. These systems have been broadly used to make quality recommendations to increase sales. However, recommender systems are vulnerable to even small data inputs of malicious information. Inappropriate products can be offered to users by injecting a few unscrupulous “shilling” profiles into the recommender system. This research proposes to identify a cluster of profiles by focusing on “filler” ratings. We examine a number of properties of such profiles, followed by empirical evidence and detailed analysis of various characteristics of the shilling attacks. We then propose a hybrid two-phase procedure for shilling attack detection. First, a multidimensional scaling approach is adopted to identify distinct behaviors that help to detect and secure the recommendation activities. Clustering-based methods are subsequently proposed to discriminate attack users. Experimental studies are conducted to show the effectiveness of the proposed method.