A Darknet is a passive sensor system that monitors traffic routed to unused IP address space. Darknets have been widely used as tools to detect malicious activities such as propagating worms, thanks to the useful feature that most packets observed by a darknet can be assumed to have originated from non-legitimate hosts. Recent commoditization of Internet-scale survey traffic originating from legitimate hosts could overwhelm the traffic that was originally supposed to be monitored with a darknet. Based on this observation, we posed the following research question: »Can the Internet-scale survey traffic become noise when we analyze darknet traffic?» To answer this question, we propose a novel framework, ID2, to increase the darkness of darknet traffic, i.e., ID2 discriminates between Internet-scale survey traffic originating from legitimate hosts and other traffic potentially associated with malicious activities. It leverages two inrinsic characteristics of Internet-scale survey traffic: a network- level property and some form of footprint explicitly indicated by surveyors. When we analyzed darknet traffic using ID2, we saw that Internet-scale traffic can be noise. We also demonstrated that the discrimination of survey traffic exposes hidden traffic anomalies, which are invisible without using our technique.