Addressing Automated Adversaries of Network Applications

Abstract

The Internet supports a perpetually evolving patchwork of network services and applications. Popular applications include the World Wide Web, online commerce, online banking, email, instant messaging, multimedia streaming, and online video games. Practically all networked applications have a common objective: to directly or indirectly process requests generated by humans. Some users employ automation to establish an unfair advantage over non-automated users. The perceived and substantive damages that automated, adversarial users inflict on an application degrade its enjoyment and usability by legitimate users, and result in reputation and revenue loss for the application's service provider.

This dissertation examines three challenges critical to addressing the undesirable automation of networked applications. The first challenge explores individual methods that detect various automated behaviors. Detection methods range from observing unusual network-level request traffic to sensing anomalous client operation at the application-level. Since many detection methods are not individually conclusive, the second challenge investigates how to combine detection methods to accurately identify automated adversaries. The third challenge considers how to leverage the available knowledge to disincentivize adversary automation by nullifying their advantage over legitimate users.

The thesis of this dissertation is that: there exist methods to detect automated behaviors with which an application's service provider can identify and then systematically disincentivize automated adversaries. This dissertation evaluates this thesis using research performed on two network applications that have different access to the client software: Web-based services and multiplayer online games.