Given the increasing scarcity of IPv4 addresses, network operators are resorting to measures to expand their address pool or prolong the life of existing addresses. One such approach is Carrier-Grade NAT (CGN), where many end-users in a network share a single public IPv4 address. There is limited data about the prevalence of CGN, despite the implications on performance, security, and ultimately, the adoption of IPv6. In this work, we present passive measurement-based techniques for detecting CGN deployments across the entire Internet, without the requirement of access to machines behind a CGN. Specifically, we identify patterns in how client IP addresses are observed at M-Lab servers and at the UCSD network telescope to infer whether those clients are behind a CGN. We apply our methods on data collected from 2014 to 2016. We find that CGN deployment is increasing rapidly. Overall, we infer that 4.1K autonomous systems are deploying CGN, 6 times the number inferred by the most recent studies.

One challenge in understanding the evolution of the Internet infrastructure is the lack of systematic mechanisms for monitoring the extent to which allocated IP addresses are actually used. In this paper, we advance the science of inferring IPv4 address space utilization by proposing a novel taxonomy and analyzing and correlating results obtained through different types of measurements. We have previously studied an approach based on passive measurements that can reveal used portions of the address space unseen by active approaches. In this paper, we study such passive approaches in detail, extending our methodology to new types of vantage points and identifying traffic components that most significantly contribute to discovering used IPv4 network blocks. We then combine the results we obtained through passive measurements together with data from active measurement studies, as well as measurements from Border Gateway Protocol and additional data sets available to researchers. Through the analysis of this large collection of heterogeneous data sets, we substantially improve the state of the art in terms of: 1) understanding the challenges and opportunities in using passive and active techniques to study address utilization and 2) knowledge of the utilization of the IPv4 space.

For more than a decade, unsolicited traffic sent to unused regions of the address space has provided valuable insight into malicious Internet activities. In this paper, we explore the utility of this traffic, known as Internet Background Radiation (IBR), for a different purpose: as a data source of Internet-wide measurements. We collect and analyze IBR from two large darknets, carefully deconstructing its various components and characterizing them along dimensions applicable to Internet-wide measurements. Intuitively, IBR can provide insight into network properties when traffic from that network contains relevant information and is of sufficient volume. We turn this intuition into a scientific investigation, examining which networks send IBR, identifying components of IBR that enable opportunistic network inferences, and characterizing the frequency and granularity of traffic sources. We also consider the influences of time of collection and position in the address space on our results. We leverage IBR properties in three case studies to show that IBR can supplement existing techniques by improving coverage and/or diversity of analyzable networks while reducing measurement overhead. Our main contribution is a new framework for understanding the circumstances and properties for which unsolicited traffic is an appropriate data source for inference of macroscopic Internet properties, which can help other researchers assess its utility for a given study.

Internet Background Radiation (IBR) is unsolicited network traffic mostly generated by malicious software, e.g., worms, scans. In previous work, we extracted a signal from IBR traffic arriving at a large (/8) segment of unassigned IPv4 address space to identify large-scale disruptions of connectivity at an Autonomous System (AS) granularity, and used our technique to study episodes of government censorship and natural disasters [1]. Here we explore other IBR-derived metrics that may provide insights into the causes of macroscopic connectivity disruptions. We propose metrics indicating packet loss (e.g., due to link congestion) along a path from a specific AS to our observation point. We use three case studies to illustrate how our metrics can help identify packet loss characteristics of an outage. These metrics could be used in the diagnostic component of a semiautomated system for detecting and characterizing large-scale outages.