The ephemeral and random nature of IPv6 client addresses presents a practical challenge to attacks that depend on Internet-wide scanning or reconnaissance – the adversary must first find the client's IPv6 address. While a well-positioned passive adversary can potentially harvest some active IPv6 client addresses, such power is typically reserved for e.g. large CDN or Internet exchange points. In contrast, prior work has shown the feasibility of a low-power entity to easily join the volunteer-based NTP Pool and harvest large quantities of active IPv6 client addresses. In this work, we develop a methodology to not only rigorously identify such IPv6 address harvesting and the entities gathering addresses, but also characterize what these entities subsequently do with the addresses. Specifically, we query all NTP Pool servers across the global Internet over the course of one-year using unique IPv6 client addresses, and monitor and correlate any later activity targeting these addresses. In sum, we identify 22 NTP Pool servers, within 4 primary clusters, that are part of larger monitoring infrastructures that utilize the gathered addresses for reconnaissance, port scanning, and service and vulnerability enumeration. To better understand the legal and ethical gray area of such behavior, we both engage with the NTP Pool operators and a cybersecurity insurance firm running one of the harvesting and scanning clusters. The NTP Pool has since integrated our system into their monitoring infrastructure to remove such NTP servers, while the firm changed its operational policy to be more transparent and provide clear opt-out mechanisms.

The five Regional Internet Registries (RIRs) provide the critical function of IP address resource del egation and registration. The accuracy of registration data directly impacts Internet operation, management, security, and optimization. In addition, the scarcity of IP addresses has brought into focus conflicts between RIR policy and IP registration ownership and use. The tension between a free-market based approach to address allocation versus policies to promote fairness and regional equity has resulted in court litigation that threatens the very existence of the RIR system. We develop WHEREIS, a measurement-based approach to geolocate delegated IPv4 and IPv6 prefixes at an RIR-region granularity and systematically study where addresses are used post-allocation and the extent to which registration information is accurate. We define a taxonomy of registration ``geo-consistency'' that compares a prefix's measured geolocation to the allocating RIR's coverage region as well as the registered organization's location. While in aggregate over 98% of the prefixes we examine are consistent with our geolocation inferences, there is substantial variation across RIRs and we focus on AFRINIC as a case study. IPv6 registrations are no more consistent than IPv4, suggesting that structural, rather than technical, issues play an important role in allocations. We solicit additional information on inconsistent prefixes from network operators, IP leasing providers, and collaborate with three RIRs to obtain validation. We further show that the inconsistencies we discover manifest in three commercial geolocation databases. By improving the transparency around post-allocation prefix use, we hope to improve applications that use IP registration data and inform ongoing discussions over in-region address use and policy.

Internet services and applications depend critically on the availability and acc uracy of network time. The Network Time Protocol (NTP) is one of the oldest core network protocols and remains the de facto mechanism for clock synchronization across the Internet today. While multiple NTP infrastructures exist, one, the "NTP Pool," presents an attractive attack target for two basic reasons, it is: 1) administratively distributed and based on volunteer servers; and 2) heavily utilized, including by IoT and infrastructure devices worldwide. We We gather complete and granular data over a nine month period to discover over 15k servers (both active and inactive) and shed new light into the NTP Pool's use, dynamics, and robustness. By analyzing address aliases, accounts, and network connectivity, we find that only 19.7

IPv4 NAT has limited the spread of IoT botnets considerably by default-denying bots' incoming connection requests to in-home devices unless the owner has explicitly allowed them. As the Internet transitions to majority IPv6, however, residential connections no longer require the use of NAT. This paper therefore asks: has the transition from IPv4 to IPv6 ultimately made residential networks more vulnerable to attack, thereby empowering the next generation of IPv6-based IoT botnets? To answer this question, we introduce a large-scale IPv6 scanning methodology that, unlike those that rely on AI, can be run on low-resource devices common in IoT botnets. We use this methodology to perform the largest-scale measurement of IPv6 residential networks to date, and compare which devices are publicly accessible to comparable IPv4 networks. We were able to receive responses from 14.0M distinct IPv6 addresses inside of residential networks (i.e., not the external-facing gateway), in 2,436 ASes across 118 countries. These responses come from protocols commonly exploited by IoT botnets (including telnet and FTP), as well as protocols typically associated with end-user devices (including iPhone-Sync and IPP). Comparing to IPv4, we show that we are able to reach more printers, iPhones, and smart lights over IPv6 than full IPv4-wide scans could. Collectively, our results show that NAT has indeed acted as the de facto firewall of the Internet, and the v4-to-v6 transition of residential networks is opening up new devices to attack.

Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely gather a large quantity of layer-two Wi-Fi identifiers by programmatically querying the eBay marketplace and applying state-of-the-art computer vision techniques to extract IEEE 802.11 BSSIDs from the seller's posted images of the hardware. By leveraging data from a global Wi-Fi Positioning System (WPS) that geolocates BSSIDs, we obtain the physical locations of these devices both pre- and post-sale. In addition to validating the degree to which a seller's location matches the location of the device, we examine cases of device movement – once the device is sold and then subsequently re-used in a new environment. Our work highlights a previously unrecognized privacy vulnerability and suggests, yet again, the strong need to protect layer-two network identifiers.

The Internet architecture has facilitated a multi-party, distributed, and heterogeneous physical infrastructure where routers from different vendors connect and inter-operate via IP. Such vendor heterogeneity can have important security and policy implications. For example, a security vulnerability may be specific to a particular vendor and implementation, and thus will have a disproportionate impact on particular networks and paths if exploited. From a policy perspective, governments are now explicitly banning particular vendors-or have threatened to do so. Despite these critical issues, the composition of router vendors across the Internet remains largely opaque. Remotely identifying router vendors is challenging due to their strict security posture, indistinguishability due to code sharing across vendors, and noise due to vendor mergers. We make progress in overcoming these challenges by developing LFP, a tool that improves the coverage, accuracy, and efficiency of router fingerprinting as compared to the current state-of-the-art. We leverage LFP to characterize the degree of router vendor homogeneity within networks and the regional distribution of vendors. We then take a path-centric view and apply LFP to better understand the potential for correlated failures and fate-sharing. Finally, we perform a case study on inter and intra-United States data paths to explore the feasibility to make vendor-based routing policy decisions, i.e., whether it is possible to avoid a particular vendor given the current infrastructure.

We present IPvSeeYou, a privacy attack that permits a remote and unprivileged adversary to physically geolocate many residential IPv6 hosts and networks with street-level precision. The crux of our method involves: 1) remotely discovering wide area (WAN) hardware MAC addresses from home routers; 2) correlating these MAC addresses with their WiFi BSSID counterparts of known location; and 3) extending coverage by associating devices connected to a common penultimate provider router. We first obtain a large corpus of MACs embedded in IPv6 addresses via high-speed network probing. These MAC addresses are effectively leaked up the protocol stack and largely represent WAN interfaces of residential routers, many of which are all-in-one devices that also provide WiFi. We develop a technique to statistically infer the mapping between a router's WAN and WiFi MAC addresses across manufacturers and devices, and mount a large-scale data fusion attack that correlates WAN MACs with WiFi BSSIDs available in wardriving (geolocation) databases. Using these correlations, we geolocate the IPv6 prefixes of >12M routers in the wild across 146 countries and territories. Selected validation confirms a median geolocation error of 39 meters. We then exploit technology and deployment constraints to extend the attack to a larger set of IPv6 residential routers by clustering and associating devices with a common penultimate provider router. While we responsibly disclosed our results to several manufacturers and providers, the ossified ecosystem of deployed residential cable and DSL routers suggests that our attack will remain a privacy threat into the foreseeable future.

Allocation of the global IP address space is under the purview of IANA, who distributes management responsibility among five geographically distinct Regional Internet Registries (RIRs). Each RIR is empowered to bridge technical (e.g., address uniqueness and aggregatability) and policy (e.g., contact information and IP scarcity) requirements unique to their region. While different RIRs have different policies for out-of-region address use, little prior systematic analysis has studied where addresses are used post-allocation. In this preliminary work, we e IPv4 prefix registrations across the five RIRs (50k total prefixes) and utilize the Atlas distributed active measurement infrastructure to geolocate prefixes at RIR-region granularity. We define a taxonomy of registration ``geo-consistency'' by comparing a prefixes' inferred physical location to the allocating RIR's coverage region as well as the registered organization's location. We then apply this methodology and taxonomy to audit the geo-consistency of 10k random IPv4 prefix allocations within each RIR (50k total prefixes). While we find registry information to largely be consistent with our geolocation inferences, we show that some RIRs have a non-trivial fraction of prefixes that are used both outside of the RIR's region and outside of the registered organization's region. A better understanding of such discrepancies can increase transparency for the community and inform ongoing discussions over in-region address use and policy.

In this paper, we show that adoption of the SNMPv3 network management protocol standard offers a unique -- but likely unintended -- opportunity for remotely fingerprinting network infrastructure in the wild. Specifically, by sending unsolicited and unauthenticated SNMPv3 requests, we obtain detailed information about the configuration and status of network devices including vendor, uptime, and the number of restarts. More importantly, the reply contains a persistent and strong identifier that allows for lightweight Internet-scale alias resolution and dual-stack association. By launching active Internet-wide SNMPv3 scan campaigns, we show that our technique can fingerprint more than 4.6 million devices of which around 350k are network routers. Not only is our technique lightweight and accurate, it is complementary to existing alias resolution, dual-stack inference, and device fingerprinting approaches. Our analysis not only provides fresh insights into the router deployment strategies of network operators worldwide, but also highlights potential vulnerabilities of SNMPv3 as currently deployed.

IPv6's large address space allows ample freedom for choosing and assigning addresses. To improve client privacy and resist IP-based tracking, standardized techniques leverage this large address space, including privacy extensions and provider prefix rotation. Ephemeral and dynamic IPv6 addresses confound not only tracking and traffic correlation attempts, but also traditional network measurements, logging, and defense mechanisms. We show that the intended anti-tracking capability of these widely deployed mechanisms is unwittingly subverted by edge routers using legacy IPv6 addressing schemes that embed unique identifiers. We develop measurement techniques that exploit these legacy devices to make tracking such moving IPv6 clients feasible by combining intelligent search space reduction with modern high-speed active probing. Via an Internet-wide measurement campaign, we discover more than 9M affected edge routers and approximately 13k /48 prefixes employing prefix rotation in hundreds of ASes worldwide. We mount a six-week campaign to characterize the size and dynamics of these deployed IPv6 rotation pools, and demonstrate via a case study the ability to remotely track client address movements over time. We responsibly disclosed our findings to equipment manufacturers, at least one of which subsequently changed their default addressing logic.

In this paper, we show that adoption of the SNMPv3 network management protocol standard offers a unique -- but likely unintended -- opportunity for remotely fingerprinting network infrastructure in the wild. Specifically, by sending unsolicited and unauthenticated SNMPv3 requests, we obtain detailed information about the configuration and status of network devices including vendor, uptime, and the number of restarts. More importantly, the reply contains a persistent and strong identifier that allows for lightweight Internet-scale alias resolution and dual-stack association. By launching active Internet-wide SNMPv3 scan campaigns, we show that our technique can fingerprint more than 4.6 million devices of which around 350k are network routers. Not only is our technique lightweight and accurate, it is complementary to existing alias resolution, dual-stack inference, and device fingerprinting approaches. Our analysis not only provides fresh insights into the router deployment strategies of network operators worldwide, but also highlights potential vulnerabilities of SNMPv3 as currently deployed.

IP geolocation has myriad applications. While a body of prior research has investigated the accuracy of geolocation databases, we take a first look at their stability. Using a large collection of snapshots from a popular geolocation database, we examine the longitudinal evolution of its location mappings and address coverage. Across different classes of IP addresses, we find that significant differences can exist even between two successive weekly snapshots - a previously underappreciated source of potential error. To assess the sensitivity of research results to the geo database instance, we examine a prior study [1] that used geolocation. Using their data and methodology, we generate results for each database instance available during their measurement period, i.e., the hypothetical results had the authors used a different snapshot. We show that the median distance of addresses considered shifted over 100km from ground truth and the coverage differed by 30% - potentially impacting the conclusions of this prior study. Based on our findings, we recommend best practices when using geolocation databases for network research to encourage reproducibility and soundness.

IP geolocation - the process of mapping network identifiers to physical locations - has myriad applications. We examine a large collection of snapshots from a popular geolocation database and take a first look at its longitudinal properties. We define metrics of IP geo-persistence, prevalence, coverage, and movement, and analyse 10 years of geolocation data at different location granularities. Across different classes of IP addresses, we find that significant location differences can exist even between successive instances of the database - a previously underappreciated source of potential error when using geolocation data: 47% of end users IP addresses move by more than 40 km in 2019. To assess the sensitivity of research results to the instance of the geo database, we reproduce prior research that depended on geolocation lookups. In this case study, which analyses geolocation database performance on routers, we demonstrate impact of these temporal effects: median distance from ground truth shifted from 167 km to 40 km when using a two months apart snapshot. Based on our findings, we make recommendations for best practices when using geolocation databases in order to best encourage reproducibility and sound measurement.

BGP communities are a popular mechanism used by network operators for traffic engineering, blackholing, and to realize network policies and business strategies. In recent years, many research works have contributed to our understanding of how BGP communities are utilized, as well as how they can reveal secondary insights into real-world events such as outages and security attacks. However, one fundamental question remains unanswered: "Which ASes tag announcements with BGP communities and which remove communities in the announcements they receive?" A grounded understanding of where BGP communities are added or removed can help better model and predict BGP-based actions in the Internet and characterize the strategies of network operators. In this paper we develop, validate, and share data from the first algorithm that can infer BGP community tagging and cleaning behavior at the AS-level. The algorithm is entirely passive and uses BGP update messages and snapshots, e.g. from public route collectors, as input. First, we quantify the correctness and accuracy of the algorithm in controlled experiments with simulated topologies. To validate in the wild, we announce prefixes with communities and confirm that more than 90% of the ASes that we classify behave as our algorithm predicts. Finally, we apply the algorithm to data from four sets of BGP collectors: RIPE, RouteViews, Isolario, and PCH. Tuned conservatively, our algorithm ascribes community tagging and cleaning behaviors to more than 13k ASes, the majority of which are large networks and providers. We make our algorithm and inferences available as a public resource to the BGP research community.

IPv6's large address space allows ample freedom for choosing and assigning addresses. To improve client privacy and resist IP-based tracking, standardized techniques leverage this large address space, including privacy extensions and provider prefix rotation. Ephemeral and dynamic IPv6 addresses confound not only tracking and traffic correlation attempts, but also traditional network measurements, logging, and defense mechanisms. We show that the intended anti-tracking capability of these widely deployed mechanisms is unwittingly subverted by edge routers using legacy IPv6 addressing schemes that embed unique identifiers.

Efforts by content creators and social networks to enforce legal and policy-based norms, e.g. blocking hate speech and users, has driven the rise of unrestricted communication platforms. One such recent effort is Dissenter, a browser and web application that provides a conversational overlay for any web page. These conversations hide in plain sight - users of Dissenter can see and participate in this conversation, whereas visitors using other browsers are oblivious to their existence. Further, the website and content owners have no power over the conversation as it resides in an overlay outside their control. In this work, we obtain a history of Dissenter comments, users, and the websites being discussed, from the initial release of Dissenter in Feb. 2019 through Apr. 2020 (14 months). Our corpus consists of approximately 1.68M comments made by 101k users commenting on 588k distinct URLs. We first analyze macro characteristics of the network, including the user-base, comment distribution, and growth. We then use toxicity dictionaries, Perspective API, and a Natural Language Processing model to understand the nature of the comments and measure the propensity of particular websites and content to elicit hateful and offensive Dissenter comments. Using curated rankings of media bias, we examine the conditional probability of hateful comments given left and right-leaning content. Finally, we study Dissenter as a social network, and identify a core group of users with high comment toxicity.

Despite the well-known existence of load-balanced forwarding paths in the Internet, current active topology Internet-wide mapping efforts are multipath agnostic - largely because of the probing volume and time required for existing multipath discovery techniques. This paper introduces D-Miner, a system that marries previous work on high-speed probing with multipath discovery to make Internet-wide topology mapping, inclusive of load-balanced paths, feasible. We deploy D-Miner and collect multiple IPv4 interface-level topology snapshots, where we find >64% more edges, and significantly more complex topologies relative to existing systems. We further scrutinize topological changes between snapshots and attribute forwarding differences not to routing or policy changes, but to load balancer "remapping" events. We precisely categorize remapping events and find that they are a much more frequent contributor of path changes than previously recognized. By making D-Miner and our collected Internet-wide topologies publicly available, we hope to help facilitate better understanding of the Internet's true structure and resilience.

BGP communities are widely used to tag prefix aggregates for policy, traffic engineering, and inter-AS signaling. Because individual ASes define their own community semantics, many ASes blindly propagate communities they do not recognize. Prior research has shown the potential security vulnerabilities when communities are not filtered. This work sheds light on a second unintended side-effect of communities and permissive propagation: an increase in unnecessary BGP routing messages. Due to its transitive property, a change in the community attribute induces update messages throughout established routes, just updating communities. We ground our work by characterizing the handling of updates with communities, including when filtered, on multiple real-world BGP implementations in controlled laboratory experiments. We then examine 10 years of BGP messages observed in the wild at two route collector systems. In 2020, approximately 25% of all announcements modify the community attribute, but retain the AS path of the most recent announcement; an additional 25% update neither community nor AS path. Using predictable beacon prefixes, we demonstrate that communities lead to an increase in update messages both at the tagging AS and at neighboring ASes that neither add nor filter communities. This effect is prominent for geolocation communities during path exploration: on a single day, 63% of all unique community attributes are revealed exclusively due to global withdrawals.

We consider the problem of discovering the IPv6 network periphery, i.e., the last hop router connecting endhosts in the IPv6 Internet. Finding the IPv6 periphery using active probing is challenging due to the IPv6 address space size, wide variety of provider addressing and subnetting schemes, and incomplete topology traces. As such, existing topology mapping systems can miss the large footprint of the IPv6 periphery, disadvantaging applications ranging from IPv6 census studies to geolocation and network resilience. We introduce edgy, an approach to explicitly discover the IPv6 network periphery, and use it to find >~64M IPv6 periphery router addresses and >~87M links to these last hops -- several orders of magnitude more than in currently available IPv6 topologies. Further, only 0.2% of edgy's discovered addresses are known to existing IPv6 hitlists.

BGP communities are widely used to tag prefix aggregates for policy, traffic engineering, and inter-AS signaling. Because individual ASes define their own community semantics, many ASes blindly propagate communities they do not recognize. Prior research has shown the potential security vulnerabilities when communities are not filtered. This work sheds light on a second unintended side-effect of communities and permissive propagation: an increase in unnecessary BGP routing messages. Due to its transitive property, a change in the community attribute induces update messages throughout established routes, just updating communities. We ground our work by characterizing the handling of updates with communities, including when filtered, on multiple real-world BGP implementations in controlled laboratory experiments. We then examine 10 years of BGP messages observed in the wild at two route collector systems. In 2020, approximately 25% of all announcements modify the community attribute, but retain the AS path of the most recent announcement; an additional 25% update neither community nor AS path. Using predictable beacon prefixes, we demonstrate that communities lead to an increase in update messages both at the tagging AS and at neighboring ASes that neither add nor filter communities. This effect is prominent for geolocation communities during path exploration: on a single day, 63% of all unique community attributes are revealed exclusively due to global withdrawals.