Format Detection Of Click Fraud In Advertising Based On User Behavior Analysis

In recent years, with the Continuous development of internet, the network has become a hot advertising carrier; the rapid development of Network advertisement has brought infinite business opportunities for advertisers and Websites. However, because valuation ways of international popular advertising CPC(Cost Per Click)have provided access to benefits for outlaws, so Click fraud phenomenon has become increasingly rampant. Click fraud exists in the online search advertisement, Click payment pattern. Click fraud ways of the network Online advertisement not only complex but also hard to detect, the detection technology has become the emerging research subject in the Internet technology in recent years.Firstly, the paper summarized the current domestic and foreign existence advertisement click valuation ways, and introduced the existence several kinds of Click fraud patterns emphatically, which aiming at the CPC valuation way. For existing click fraud phenomenon, existing click fraud detection method is mainly by relative static identity and role as the foundation, not very good note the future trend of user behavior, lack of necessary trust, and some detection methods take the serious sacrifice network users Internet experience as a cost. Through the analysis and comparison of these detection methods, the author proposed advertisement click fraud detection method based on the user behavior. This method mainly studies the network user click behavior, and then according to user's behavior conducts dynamic control decision-making. in the process of this method, the author first uses certain techniques to gain clients to access data, and then select needed data items of the detection method to establish click stream data warehouse, And then the author use data mining the Bayesian network classification method to forecast each time customer click valid rank probability, and finally combines the customers click forecasting result and the gambling control to analyze the bilateral payoff matrix, calculated attribute mixed the Nash balanced strategy based on the user different behavior, finally judged users each click behavior real validity .At the end of this paper, combining the examples, the author checked out the advertisement click fraud detection method based on the user behavior, and achieved good effect. The result of this paper has an important theoretical significance on quantizing analysis legality of the user advertising click behavior. Therefore, also has an important guiding role in the actual network applications...