A significant proportion of Web traffic is now attributed to Web robots, and this proportion is likely to grow over time. These robots may threaten the security, privacy, functionality, and performance of a Web server due to their unregulated crawling behavior. Therefore, to assess their impact, it must be possible to accurately detect Web robot requests. Contemporary detection approaches, however, may cease to be effective as the behavior of both robots and humans evolves. In this paper, we present a novel detection approach that is based on the contrasts in the resource request patterns of robots and humans. The proposed scheme, which relies on an invariant behavioral difference between humans and robots, builds on the lessons from contemporary approaches. We demonstrate that the proposed approach can accurately detect Web robots and argue that it is expected to remain effective even as they continue their rapid evolution.