SHILLING ATTACKS DETECTION BASED ON SPAMMER BEHAVIOURAL METHODS USING MACHINE LEARNING
Keywords:K.Pavithra, K.Puvarshini Nanthithaa, M.Sanjith Gunal, S.Vasanthasena, Prema P
The online reviews can change decision of the customer and they can finalize a product by comparing with the brands of products, the customer can select the product and satisfy their requirement only if the reviews are not fake. On the contrary, if the reviews are fake then it misleads the customer. To solve this problem the identification of the fake opinions from the customer needs to be extracted. Users behaviors are extracted based the semantical analysis of his review content for the purpose of identifying the review as fake or not. The reviews are extracted from the web for a particular product, along with the reviews of several other information related to the reviewers also been extracted to identify the fake reviewers using k-means clustering classifier and Information Gain. Signification of the features on the decision is validated using information gain. Experiments are conducted on exhaustive set of reviews extracted from the web and demonstrated the efficiency of the proposed approach.