Abstract: Online advertisement is not only the main sources of income of profit for internet giants,but also provides powerful economic support for the internet development.The commonly used methods of click fraud detection,which are based on the features of client’s behavior,may lead to inefficiency in fraud detection due to redundant features.To solve this problem,a fraud detection method which combines feature selection with classification method was proposed.According to the feature attributes set of fraud advertisement which is found through training set,attribute significance is sorted by Fisher score method.The important attributes is selected and the SVM algorithm is lastly introduced into classification based on these important attributes.Experiments on real data set demonstrate that the proposed detection method is feasible and valid.

Key words: Click fraud,Fisher score,Support vector machine,Feature selection