A game theoretic approach for inspecting web-based malvertising

Web-based advertising system has become a convenient and efficient channel for advertisers to deliver ads to targeted Internet users. Unfortunately, this system has been exploited by cybercriminals to disseminate malware to an enormous number of end-users and their vulnerable machines. To protect their malicious ads and malware from detection by the ad network, malvertisers apply a variety of evasion techniques such as fingerprinting the execution environment, redirecting to compromised IP addresses, and malware polymorphism. On the other hand, the ad network can also apply inspection techniques to spoil the malvertiser's tricks and expose the malware. However, both the malvertiser and the ad network are under the constraints of resource and time. In this paper, we aim to apply game theory to formulate the problem of inspecting the malware inserted by the malvertisers into the Web-based advertising system. We design a normal form game between the malvertiser and the ad network, define their strategies and payoff functions, and compute their pure-strategy and mixed-strategy Nash equilibria. We use numeric simulation to evaluate our game theoretic models, and derive several insights from the results that can serve as guidelines for the ad network to decide its best inspection strategy.