SLAM - Strategies for Large-Scale IPv6 Active Mapping
This is a collaborative project co-led by Professor Robert Beverly of Naval Postgraduate School's Center for Measurement and Analysis of Network Data. An abbreviated version of the original proposal is shown below. For the full proposal for "SLAM - Strategies for Large-Scale IPv6 Active Mapping", please see the proposal in PDF.
Principal Investigators: kc claffy Aaron Schulman
Funding source: CNS-1901517 Period of performance: September 1, 2019 - August 31, 2022.
1 IntroductionNetwork maps - including graphs of routers, links, locations, and other meta-data - are as fundamental and crucial to network operations, engineering, and research as physical road maps are to driving. It is no surprise, then, that the networking community has expended significant effort over the past several decades developing tools and techniques, both active and passive, to gather Internet topologies (see e.g. [35,66,38] and references therein for an overview of this body of work). Today, several organizations, including our own, collect, infer, and annotate the IPv4 Internet topology - producing network maps that have been instrumental in supporting and advancing science, policy, research, and engineering. Internet topology data has been used in everything from censorship inference to geolocation to content distribution to security. Less attention has been paid to the IPv6 topology. Part of the lack of focus on IPv6 has historically been due to low IPv6 adoption and use rates. However, fundamental differences from IPv4 hinder comprehensive and representative IPv6 topology measurement:
- A massive address space (2128) that is sparsely populated ( ∼ 249 addresses currently advertised ) and cannot be exhaustively scanned. Researchers are only now experimenting with and finding viable methods for selecting measurement targets in IPv6 [29,48,32].
- Mandated Rate limiting in ICMPv6  imposes a measurement catch-22: probing faster to sample more of the space is apt to induce more rate-limiting, and is thus self-defeating.
- Address agility that accommodates regularly shifting customers across IPv6 prefixes, and widespread use of temporary private addresses [49,52] which makes many targets ephemeral.
- Task A: New Measurement Strategies To Meet IPv6-specific Challenges: We recently introduced a new active measurement technique, Yarrp , and demonstrated  its ability to discover > 1.3M unique IPv6 router interfaces from a single vantage in a single day, an order of magnitude more topology than state-of-the-art IPv6 mapping systems using hundreds of vantage points. To operationalize this capability for use in scientific research, we need to refine the technique to support distributed deployment across hundreds of points, decouple probing from response collection, incorporate our previous work on intelligently directing IPv4 probing [10,13], and improve efficiency (e.g. topological yield measured in interfaces or routers per unit time, per trace, or per packet) and performance (e.g. metrics of speed, recall, per-hop responsiveness, reachability, network load, host load, minimizing complaints, etc). We also propose to map the IPv6 infrastructure of mobile providers, a reported driver of IPv6 adoption [58,5,64].
- Task B: Innovations in IPv6-specific algorithms to infer router-level topologies: Our previous collaboration yielded the first IPv6 address alias resolution technique [14,43], which reduces the interface-level graph to a router-level graph. Unfortunately, due to router implementation differences and network filtering policies, our technique only works for a subset of all deployed routers (approximately one-third of probed interfaces ).1 We will explore the use of multiple alias resolution techniques, e.g. , at Internet scale, as well as develop new IPv6 alias resolution techniques that leverage DNS hostname conventions to infer aliases. We will also develop methods to generate candidate aliases to discover previously unknown interfaces, which will facilitate comparative graph analyses between IPv4 and IPv6 router-level topologies, illuminating the degree of inter-dependence and shared risks - or lack thereof - between IPv4 and IPv6 infrastructure.
- Task C: Analysis and Remediation of Security and Privacy Risks: The most disturbing discovery thus far from our IPv6 active topology probing is pervasive use of EUI-64 addresses , which encode the interface's hardware MAC address into the low-order 64 bits of the IPv6 address as a mechanism to provide uniqueness. To mitigate the risks of using a static hardware identifier at the network layer (thus visible to a remote adversary), modern operating systems have adopted privacy extensions that utilize pseudo-random and ephemeral addresses . Thus, we were surprised that approximately 45% of router hop addresses found in our initial Yarrp6 high-speed survey were EUI-64 addresses, assigned to Customer Premise Equipment (CPE) routers in large, homogeneous IPv6 deployments. The use of EUI-64 addresses to number network infrastructure triggers immediate concerns. First, MAC addresses leak information not only about the device manufacturer, but also potentially the model . In these cases, attackers can perform targeted attacks, particularly devastating for generally ill-maintained consumer-grade equipment. Further, CPE using EUI-64 addresses can compromise privacy-focused efforts to circumvent IP-based tracking . We plan to not only investigate this phenomenon, but also initiate efforts to resolve the vulnerability among providers, manufacturers, and work within standards bodies.
2 MotivationProgress along these three fronts can help provide a solid foundation for networking and systems research that depends on network topologies. More broadly, the resulting data can enable not only researchers, but also policy makers and regulators to better understand the network. In this section, we provide specific benefits our proposed IPv6 topology research will provide for: i) resolving the IPv6 adoption paradox; ii) science and society; and iii) engineering and operational network practice.
2.1 Resolving the Adoption ParadoxMeasuring IPv6 adoption is not straightforward . Some proposed metrics of adoption include the number of networks (e.g. ASes) advertising IPv6 BGP prefixes, the fraction of content web sites with IPv6 DNS records, counts of client IPv6 web accesses, or the volume of IPv6 traffic on a network link. Further complicating accurate IPv6 adoption estimations are variations due to methodology and observation point. In many countries, mobile networks are reportedly driving IPv6 adoption and traffic . Apple has required all iOS apps to work in a IPv6-only world since June 2016 . T-Mobile has even presented on their efforts to turn off IPv4 entirely . Verizon Wireless claims to be one of the largest production IPv6 deployments, with a large IPv6 user base (handsets) . In fact, Akamai reports that Verizon is the largest contributor of IPv6 requests to their CDN platform, estimating Verizon's IPv6 "adoption" rate at 83.6% , with no definition of "adoption." Similarly, Akamai's academic publications claim "a striking volume of World-Wide activity on IPv6 today." Yet, these reports stand in contrast to published measurements of observable IPv6 traffic volume: approximately 1% IPv6 traffic in 2018 on three U.S. backbone links ; 2-3% on AMS-IX . One benefit of improved IPv6 topology maps that reflect actual reachability is to better reconcile these wildly divergent reports of IPv6 penetration. Rather than relying on aggregate and imprecise statistics used for marketing, we require systematic, broad, and representative, i.e. scientific, IPv6 measurements. For example, one explanation for the disparity in adoption statistics could be that the IPv6 traffic is traveling between directly connected private networks (e.g., Verizon and Facebook) and thus does not cross the public Internet. But the ways in which IPv6 is being deployed to mobile customers has important implications for users, and for the future of the Internet. Does IPv6 provide customers with unimpeded end-to-end connectivity? How much of the IPv6 Internet can successfully be reached? Are IPv6 paths within mobile networks shorter (either topologically or geographically) than over IPv4? What are the privacy and security implications of providing customers public IPv6 addresses? We can answer these questions by performing active topology mapping both toward mobile networks, as well as from and within mobile networks (§4.1). In the former case, we will leverage our high-speed probing infrastructure in conjunction with carefully constructed mobile network seed sets. To obtain traces from mobile networks, we will implement a mobile version of Yarrp, initially targeting the Android platform.
2.2 Benefits to Science, Engineering, and SocietyBetter tools and methods for IPv6 topology measurement will benefit not only scientific study of the Internet and other large scale networks, but also inform network engineering, security, and operational practices.
2.3 Network EngineeringFrom a network engineering perspective, better IPv6 mapping is crucial to:
3 Background and Related Work in IPv6 Topology MeasurementWhile decades of research have developed and refined active IPv4 topology discovery (e.g. [59,38,26,40,13]), none of this work addresses IPv6-specific mapping challenges. We briefly review prior work to establish context for our proposed research.
3.1 Measurement MethodsBGP-based measurement. Prior IPv6 topology analysis has largely avoided active probing due to the sheer size of the address space and the sparsity of infrastructure within it. Using passive BGP data, Dhamdhere et al. found fewer than 50% of AS-level paths in 2012 were identical between IPv4 and IPv6, with a single AS (Hurricane Electric) clearly dominant in the IPv6 topology . In 2013, Czyz et al. found only 19% of IPv4 ASes also supporting IPv6, most of them large transit networks . Traceroute-based measurement. Two production platforms continually perform active IPv6 topology measurement. CAIDA's Archipelago (Ark)  and RIPE Atlas . These systems send Paris traceroute probes  toward the ::1 address in each IPv6 prefix present in a global BGP table.2 Using BGP prefixes to guide target selection captures topological breadth, but not depth, i.e. it fails to discover subnetting. Our 2014 study exhaustively probed an address in each routed /48 - 400M traces from 26 vantage points found more than double the number of unique interfaces compared the coarser granularity probing based on global routing tables . Our recent development of the first IPv6 router alias resolution methods [14,43] relied on these traceroute data sets, but did not solve the problem of creating input lists of IPv6 interfaces.
3.2 State of the Art in High-Speed Topology Probing
Figure 1. Probing strategy and rate versus responsiveness. ICMP6 rate-limiting is evident, as well as per-hop differences. Yarrp6's randomly permuted probing order improves per-trace responsiveness.
Figure 3. Evaluating probing strategies: discovery vs. probe packets (log-log). Intelligent probing improves discovery.
4 Research Plan
4.1 Task A: New Measurement Strategies To Meet IPv6-specific ChallengesOur first task is to advance the state-of-the-art in IPv6 measurement to provide, deploy, and apply the most "comprehensive, pervasive, accurate, and usable measurement capabilities". This task includes techniques to improve efficiency and performance; enabling IPv6 Internet topology mapping from distributed VPs; decoupling probing from response collection; and adaptation of our work on intelligently directing IPv4 probing [10,13] Techniques to improve efficiency and performance: Currently, Yarrp provides a simple approach to large-scale scanning without the complexity of intelligently driving the probing. This approach favors recall - finding the most infrastructure possible in the shortest amount of time - over efficiency. Yarrp's statelessness implies that it does not stop probing after reaching a destination or after not receiving replies from several hops in sequence (the "gap limit"). Figure 2b illustrates the limitation of this simple approach: Yarrp6 outperforms Doubletree at TTL hops below the configured Doubletree midpoint, but as TTL increases, Yarrp6 yields lower per-packet responsiveness. In short, Doubletree stops probing after the gap limit or after reaching the destination, but Yarrp continues probing, past the point where it will likely receive a response. We propose to experiment with our recently introduced idea of a "fill mode,"  and rigorously investigate its use, performance, or efficiency gains. In fill mode, if the Yarrp6 listener receives a response for a probe sent with hop limit h that exceeds the max probing TTL, Yarrp6 immediately sends a new probe toward the destination with a hop limit of h+1 to fill the gap. While these additional probes are not randomized, fills are uncommon, and occur at the tail of the path where the effect of sequential probing has the least impact. We are especially interested in cases where the fill mode prematurely stops, for instance when there is an unreachable hop past the configured TTL m, but a subsequent reachable hop at m+k that is not probed. While maintaining a count of unreachable hops is not possible in a stateless implementation, we plan to study mechanisms to enhance probing efficiency. This presents an opportunity for synergy if we can merge elements of Doubletree into Yarrp. Parallelized Probing: The sheer size of the IPv6 address space implies that mapping speed will always be a limiting factor in comprehensive probing. Parallel execution of the Yarrp process on distinct vantage points will provide the most significant practical speed improvement. A significant advantage to Yarrp's design is the potential to easily randomize and distribute the probing to multiple vantage points with negligible coordination and communication overhead. However, distributed Yarrp is an open research problem. While the entire domain of targets and TTLs can be subdivided among vantage points, we wish to ensure that different vantages are not responsible for different portions of the TTL space for the same target. Otherwise, the discovered hops toward a destination will not reflect the true path, but rather interspersed portions of paths. A hypothetical solution is a simple scheme that assigns each of n vantage points a unique identifier. If each vantage point uses the same key, it will generate the same randomly permuted sequence of targets and TTLs. As each vantage point works through its locally computed sequence, it checks whether it is responsible for the target via a simple modulo computation (e.g. IP % (n−1) =? vp). For additional randomness, the IP may be hashed prior to this check. But this approach wastes CPU cycles (and hence reduces Yarrp's probing speed) as Yarrp instances skip portions of the permutation space they are not responsible for. We therefore propose to investigate more elegant solutions to distributing Yarrp. To explore techniques and research toward parallelizing and distributing Yarrp, we will deploy Yarrp on CAIDA's Ark platform. Ark can help inform important systems questions that are required to produce the best possible topology maps, including determining the best way to retrieve and aggregate data from the distributed monitors, and how to manage vantage points with varying probing rates. Finally, we will validate and run Yarrp in a distributed fashion on the live Internet. Decoupling Probing and Receiving: Yarrp's stateless nature opens the possibility to decouple Yarrp's probing engine from collection. Because Yarrp recovers state from information contained within the probe responses, the machine that receives probe responses does not have to be the one that sends the probes. To expand measurement coverage, the Yarrp prober may put the source address of a remote collector machine inside probes, assuming common ownership of the machines or permission to spoof the source IPv6 address. CAIDA maintains vantage points with permission and capability to spoof for measurement research, so we can experiment with this capability. We hope to explore paths that currently block responses, reveal AS-internal infrastructure, and gain additional visibility into IPv6 exchange point connectivity. Intelligent target selection: The studies described in Section 3.2 have advanced our understanding of how to identify address structure and predict likely portions of active IPv6 address space, e.g. as targets of IPv6 measurement surveys. However, they have focused on IPv6 end-hosts rather than core network infrastructure or finding targets for active topology measurements. Our experiments with 6gen  generated many permutations of likely hosts within single /64s. This subtask will adapt our previous work on intelligently directing IPv4 probing [10,13] to our Yarrp system. We will use similar techniques to discover structure within the high-order bits, or network portion, of the router infrastructure address we discover from prior rounds of probing to drive the selection of targets to probe in subsequent rounds. Open research issues in target selection include how to best derive a single set of targets for production deployment, how to utilize hitlists where addresses within the hitlist change rapidly over time, and, more generally, how to improve target selection to maintain efficiency and coverage (high per-probe recall). Mapping Mobile Providers: As described in the motivation, mobile providers have claimed large IPv6 infrastructure deployments and large numbers of IPv6 customers. Similarly, content providers have claimed large numbers of IPv6 requests, clients, and "striking" volumes of IPv6 traffic. To address the larger question of resolving these adoption claims with other reported metrics that show lower IPv6 usage, we propose a two-pronged effort to map the IPv6 infrastructure of mobile providers. First, we will identify IPv6 prefixes belonging to mobile providers and delineate between IPv6 prefixes used to number mobile infrastructure versus IPv6 prefixes that are assigned to customers. While seemingly simple, automatic (i.e. scalable) labeling of prefixes as either mobile or not is an inference problem that has not been solved. We propose to utilize an analysis of HTTP user-agents observed on backbone links and other passive observation points to find prefixes that are predominantly used by devices with a mobile user-agent string. Since a mobile user-agent string can as easily originate from a residential broadband address when the device is on WiFi, we will build a statistical model of user-agents per prefix to identify regions of mobile IPv6 user addresses. Further, we will leverage the proposed DNS research in Task B (§4.2) to see if hostname analytics can help. Second, we will utilize Yarrp to perform high-speed mapping of identified mobile networks. Yarrp's speed comes into play in two ways here: to cover as much of the provider's address space as possible, and to identify ways that providers employ ephemeral address and address agility. Finally, we propose to utilize general purpose computers tethered to mobile devices to serve as the source of Yarrp probes originating from the mobile edge.4 Our initial experiments from Yarrp measurements from two U.S. mobile carriers have already yielded some fascinating insights, including different IPv6 paths than IPv4, shorter IPv6 paths as compared to IPv4, and even private and link-local IPv6 addresses along paths.
4.2 Task B: innovations in IPv6-specific algorithms to infer router-level topologiesAlias resolution - the problem of determining whether two or more interface address belong to the same physical router - is crucial to producing router-level topologies. Router-level topologies are frequently more useful and representative than raw IP interface graphs, especially when considering network structure and resilience. Integration of existing and new methods to improve alias resolution completeness and accuracy. Our tool, speedtrap , is currently the best available method to resolve IPv6 aliases; it works by inducing IPv6 routers to send fragmented packets, and comparing the fragment identifiers between probed interfaces. Thus, speedtrap only works on routers that respond with ICMPv6 Packet-Too-Big messages, and if such packets are not filtered along the path, and only when routers implement particular IPv6 fragmentation behavior in their control plane. For instance, we find that while speedtrap works well for Cisco devices, it fails with Juniper routers, which implement random fragment identifiers. We propose to explore new methods of resolving IPv6 aliases, including combining speedtrap with techniques developed by other groups, including UAv6  (whose author begins a postdoc at CAIDA in late 2018). DNS naming convention inference to support topology mapping. We propose to leverage hostnames and DNS PTR records corresponding to interfaces of known aliased routers to infer router naming conventions. We will use statistical learning primitives to automate inference of per-provider regular expressions (REs) that capture a given provider's method of assigning names to router interfaces. We can also leverage this DNS-based approach to generate candidate aliases that we can subsequently probe with other alias resolution techniques. Our collaboration with Matthew Luckie at U. Waikato on this effort thus far suggests the promise of this approach, which significantly generalizes manual heuristic approaches to understanding names, e.g. as pioneered in . We describe the intuition behind our idea as follows. First, given a set of regular expressions (REs) that capture the per-provider naming convention, we will use these REs to generate candidate router names. As a proof-of-concept, we plan to permute the space of integers within known names, and then determine whether a corresponding AAAA (DNS) record exists for each candidate name. We will use active probing methods to determine if the candidate names with DNS records are new aliases of routers we have already identified, or an interface of a previously unknown router. To illustrate, consider the following interfaces discovered through CAIDA's IPv6 traceroute campaigns :
xe-0-1-0-0-ce-bothwaak14w.bothell.wa.seattle.comcast.net xe-1-1-0-0-ce-bothwaak14w.bothell.wa.seattle.comcast.net xe-4-0-0-0-ce-bothwaak14w.bothell.wa.seattle.comcast.netGiven these names as input, we can expand the four interface identifier digits ("xe-X-X-X-X") to generate all possible 104 permutations. The intuition here is that the digits typically signify facilities, routers, router line cards, and router interface numbers. For instance, "xe-2-3-0-0" might denote the second router, third line card, and first interface on the line card in the first data center. In the above example, after generating these permutations, we discover that:
xe-1-0-0-0-ce-bothwaak14w.bothell.wa.seattle.comcast.nethas an authoritative DNS record, and alias resolution confirms that it is an alias of the three other interfaces. Our initial experiments with this idea have convinced us that not only can the DNS be used to associate previously unassociated interfaces, it can also be used to discover entirely new interfaces and aliases, i.e. those not previously known via the traceroute campaign. Similarly, we will use other existing list of DNS names, e.g., gathered via walking reverse DNS zones , to test candidates for alias resolution. Finding IPv4/IPv6 Aliases. In an IPv6 world, aliases extend beyond identifying IPv6 interface addresses that belong to the same router to the problem of inferring whether an IPv4 address and an IPv6 address are aliases, i.e. whether they are assigned to the same interface. Such inference is crucial to understanding the extent to which the IPv6 infrastructure depends (or does not) on the IPv4 infrastructure, and whether these networks share fate e.g. during attacks. Our previous work used TCP timestamp skew to find server siblings: IPv4 and IPv6 addresses that belong to the same physical server . We inferred cross-protocol siblings by using the common upper layer, namely TCP, and included a measurement study against DNS and web servers. Our technique leveraged the well-known observation that the clock skew of machines, as exposed remotely by TCP timestamps, can be used to fingerprint physical machines. However, routers typically do not expose open TCP ports, preventing use of this method to find sibling addresses on routers.5 But we can use insights from these studies to develop novel methods for finding IPv4 to IPv6 siblings. First, we will investigate the use of DNS regular expressions (described above) to find alias candidates. We will also investigate other identifiers, including MPLS labels , and TTL-based router signatures  to find cross-protocol aliases. Topology Analysis. The combined effort of our next-generation IPv6 topology probing, alias resolution, and alias discovery methods, along with reliable production deployment (see §4.2) will allow us to produce the most complete IPv6 topologies currently available to researchers. Once we have our new measurement capabilities deployed, we will analyze the resulting data sets by comparing IPv6 snapshots over time, and comparing IPv4 and IPv6 topologies. Comparing IPv6 Topology Snapshots. Because Yarrp probes links in a randomly permuted, responses return in a similarly random order. This approach inherently decouples probing operation from path reconstruction - Yarrp can only reconstruct the full path to any single destination after all probing completes, for all destinations. This path reconstruction can require significant computation, but can occur offline and is not time critical. This decoupling means probing can be very fast, which brings us closer to the goal of obtaining true topology snapshots, where we can compare differences to capture fine-grained topology dynamics. We propose such comparisons of IPv6 network topologies to better understand not only the IPv6 network's behavior, but also help shed light on e.g. short-lived route hijacking events, discover backup links that are only exposed during transient outages, inform IP geolocation, and gain rapid insight into geo-political events such as censorship. Comparing v4/v6 Topologies. Perhaps the most interesting analysis will leverage our expertise in collecting and collating IPv4 topologies to perform comparative analyses of the IPv4 and IPv6 logical networks. While prior work has compared IPv4 and IPv6 at the AS-level through passive BGP analysis, accurate IPv6 topologies would enable the first comparative studies of these two networks at the interface and router-level. We will not only be able to compare the structural characteristics of the IPv4 and IPv6 topologies, but also to analyze the extent to which they share physical infrastructure, and hence fate. As the IPv4 and IPv6 networks converge, we will quantify the extent to which not only attacks, but disruptions and bugs, that affect one infrastructure will impact the availability of the other. A long-standing initiative of CAIDA has been to not only perform research, but provide infrastructure and resources to perform long-running continuous measurements. The data from these production measurement campaigns has proved invaluable - both for analyzing longitudinal trends and understanding how properties of the network evolve, but also for retroactively analyzing particular important events, e.g. attacks, censorship, or peering changes, e.g. [23,44]. We will incorporate the research results of Task A into our publicly available Yarrp tool [11,15], and perform the necessary systems development to bring Yarrp into a production state on CAIDA's Archipelago platform . A new production IPv6 topology measurement system represents a significant systems development effort including: making the tools robust, intelligently distributing measurement tasks among a large pool of (currently more than 200) vantage points, handling much higher probing rates (10× more packets/sec), and processing and maintaining much larger volumes of topology data. These development efforts are essential to operationalizing a sustainable system that can provide invaluable continuous IPv6 topology data to serve as a foundational component supporting future network research.
4.3 Task C: Analysis and Remediation of Security and Privacy RisksAn unexpected finding of our initial IPv6 active topology discovery campaign is non-trivial numbers of EUI-64 addresses , which encode the interface's hardware 48-bit IEEE Media Access Control (MAC) address  into the low-order 64 bits of the IPv6 address as a mechanism to ensure uniqueness . While this standard provides a simple mechanism for hosts to automatically configure and use a unique IPv6 address on a subnet , it effectively leaks the hardware MAC address of the host to higher layers of the network stack. Typically, MAC addresses are only visible to passive observers in the broadcast domain of the same layer-2 subnet. But any layer-3 device along the communication path can observe these EUI-64 IPv6 addresses - and, in turn, observe the host's MAC address. These addresses are static, associated with the physical device, and if known, both leak device details and act as a unique tracking ID. Modern operating systems have evolved to mitigate this risk by randomizing the host bits of their IPv6 addresses according to the privacy extensions standard . Not only are these addresses randomized, they are highly ephemeral . While end-hosts have thus largely abandoned the use of EUI-64 addresses, Customer Premise Equipment (CPE) infrastructure (e.g. home routers and gateways) still extensively use such addresses. Traceroute probing reveals these addresses, introducing two vulnerabilities: i) the ability to identify the manufacturer and model of a device, thereby permitting targeted attacks [46,47]; and ii) the ability to track users despite efforts to prevent such tracking. More specifically, not only are the host bits of a client's IPv6 address, but their entire assigned network prefix, is similarly ephemeral . Many ISPs regularly change client IPv4 and IPv6 addresses for privacy reasons . However, while the client's end-host IPv6 address is changing, the address of her home gateway is not, so an attacker can perform traceroutes to the client can determine that it is the same client, and track the client's assigned addresses over time. We will take a three-step process to investigating the prevalence and risk of EUI-64 addresses in IPv6 infrastructure. Longitudinal Characterization: Our initial IPv6 topology survey  produced responses from 651.4k unique EUI-64 interface addresses, or 45% of all hops. Of these, 59% were from one of just two manufacturers, and 99.9% of those devices were in just two ISP networks, each in different countries. In both cases, WWW content suggests they were Customer Premises Equipment (CPE) routers in large IPv6 deployments. Our first sub-task is longitudinal characterization of EUI-64 SLAAC addresses discovered through operational probing by three Internet wide measurement platforms: Ark , Atlas , and Yarrp6 . We will study the evolution and prevalence of EUI-64 addresses, as well as classify them by networks, providers, and geo-regions hosting them. Our initial analysis of CAIDA IPv6 topology data in 2018  suggests that infrastructure EUI-64 addresses are pervasive: 70% of observed ASes have them, 20% have 25 or more. CAIDA's probing of the base address (::1) for this data imply these numbers are a lower-bound; the true number of EUI-64 addresses deployed is likely much higher, which targeted high-speed Yarrp6 probing can help reveal. The distribution of MAC addresses among the prefixes of different providers exhibits oddities that bear further investigation. For instance, particular MAC addresses are disproportionately represented in some networks and countries, suggesting the existence of multiple devices with the same MAC address. Such MAC address duplication has been anecdotally reported on in the past, but not formally studied. Addresses may be duplicated by manufacturers of inexpensive equipment that do not wish to pay for new IEEE allocations, or may be reused after the expected useful lifetime of the hardware has passed. We hope to shed light on this practice. Given a set of passively observed EUI-64 addresses, we will perform active measurements toward combinations of the provider's prefixes and the EUI-64 address, searching for the client's CPE within the provider's address space. Further, many EUI-64 addresses concentrate among subnets of globally advertised prefixes (i.e. more specific than the advertised prefix). We believe that high-speed tools such as Yarrp6 can efficiently, effectively, and exhaustively probe these subnets in order to discover previously unknown CPE infrastructure. Proof-of-concept experiments thus far have been encouraging: random scanning of the /64 prefix containing the most EUI-64 addresses in a month's worth of passive CAIDA data  resulted in 18 responses from EUI-64 IPv6 addresses, where the MAC addresses were previously unobserved.
4.4 Granular Mapping of CPE MAC Addresses to Hardware47]. The high-order three bytes of a MAC address, termed the Organization Unique Identifier (OUI), are allocated to a manufacturer, for use as they wish. Given samples of known devices and their MAC addresses, we can build a map of likely allocations, and use it to predict the device model given an unknown MAC address. In prior work , we created such a mapping for mobile devices since a passive observer can observe MAC addresses of 802.11 WiFi networks. Allocations of contiguous blocks to distinct device models are also present among access point (AP) manufacturers. Figure 4 shows the inferred allocation of the C0:C1:C0 OUI owned by Cisco, with 17 models of APs. We find 248 distinct contiguous ranges dispersed throughout the OUI; the Linksys E1000 constitutes the largest contiguous block of any single device (2.4M addresses). About 60% of the OUI was allocated, with a mean of ∼ 41K addresses per block. Remote discovery of IPv6 infrastructure EUI-64 allows creation of analogous fine-grained mappings of MAC addresses to model for core network infrastructure. The ability to determine the specific model of a piece of network equipment, in addition to the manufacturer, poses a security vulnerability, namely enabling targeted attacks [19,41]. We propose to first research the scope of the problem by building the first comprehensive mapping of CPE MAC addresses to fine-grained manufacturer and model. Key to our ability to produce accurate mappings is having ground-truth for specific OUIs,i.e. we must know the specific device manufacturer and model corresponding to a sample of MAC addresses within the space. Many ground-truth examples are available on web sites, email lists, and discussion forums, including by homeowners having trouble configuring CPEs, and manufacturers trying to help them. We will use basic search engines to automate discovery of these MAC addresses and their corresponding device models where we can, and manually populate data otherwise. We will use the various search engine APIs to issue queries for a particular device model and mine the results for MAC addresses. Since MAC addresses have a distinct format, we can discover likely addresses corresponding to that model.
4.5 RemediationFinally, we will demonstrate the potential to exploit this information, as well as understand the scope of the security and privacy impacts. We will run on-demand traceroutes to IPv6 clients that access the web and find those that, over time, have a common last hop address with an EUI-64 address. To perform this experiment, we will use CAIDA's web server and partner with other organizations to expand coverage, e.g. we have partnered with Akamai in the past, and made use of their large web footprint in other research. We believe that these leaked MAC addresses via EUI-64 number of infrastructure represent a currently unknown security vulnerability, and it is our responsibility to responsibly disclose the vulnerability and work with providers and networks at remediation. The community should carefully consider the implications of these router-address-assignment practices as a router's source address, alone, e.g. when sending ICMPv6 error messages, can disclose private details. We will engage with our network of engineers and network managers that we have cultivated over the years, as well as present our findings to equipment manufacturers, NANOG, and the IETF.
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Footnotes:1Notably, speedtrap does not work on Juniper routers. 2CAIDA's Ark probing relies on scamper, the current state-of-the-art topology probing tool, which supports Paris traceroute . 3In contrast, traceroute was never designed for Internet-wide probing or gathering complete Internet topologies, but rather as a diagnostic tool for network operators to probe individual paths. 4In contrast to IPv4, we do not observe NAT used for IPv6 when tethering. 5Although Czyz find approximately 4% of 25k dual-stack routers respond on the ssh and BGP ports .
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