earticle

영어

Besides unsupervised feature, universality serves as another important factor determining the practical value of attack detection technology. Considering the difficulty of possessing both features for the existing attack detection techniques, this paper reveals the latent factors invoking missing ratings under the non-random-missing mechanism and further combines these latent factors with Dirichlet process in the framework of probabilistic generative model, thus proposes the Latent Factor Analysis for Missing Ratings(LFAMR)model. Based on performing user clustering with this model, this paper achieves the goal of attack detection by presenting the method for identifying attack cluster in ideal situation. Experimental results show that comparing with the existing detection techniques, LFAMR is more universal and unsupervised, and it can effectively detect shilling attacks of typical types and their derivatives even in lack of the apriori inputs such as user cluster numbers.