An abbreviated version of the original proposal is shown below.
Funding source: NSF C-ACCEL OIA-1937165. Period of performance: September 1, 2019 - May 31, 2020.
2 Appropriateness for Convergence Accelerator Program
3 Components to Support an Open Knowledge Network
4 Project Plan
4.1 Task 1: Build Team and Define OKN: People, Principles, Purpose
4.2 Task 2: Design KISMET prototype: Patterns, Protocols, Production
File translated from TEX by TTH, version 4.03.
On 13 Sep 2019, 23:17.
2 Appropriateness for Convergence Accelerator Program
3 Components to Support an Open Knowledge Network
4 Project Plan
4.1 Task 1: Build Team and Define OKN: People, Principles, Purpose
4.2 Task 2: Design KISMET prototype: Patterns, Protocols, Production
1 IntroductionIn the mid-1990s, the U.S. government launched strategic industrial policies to promote competition, and thus innovation, in the emerging Internet transport and domain name industries. In the 25 years since, the Internet's reach has expanded to over 3B people around the world, and continues to grow. As the Internet has become critical infrastructure, society has grown increasingly exposed to the fundamental security weaknesses embedded in the underlying TCP/IP architecture, as well as new vulnerabilities, some of which are rooted in the competitive political economy in which the Internet operates. Despite herculean efforts across industry, government, NGOs, and academia, we still lack an understanding of the effectiveness of risk-mitigating efforts, or even to what extent defenses have been deployed. Although many data sources exist in various forms, the volume and complexity of data is overwhelming. Knowledge is elusive, and where it emerges, often proprietary. Although security issues now capture daily headlines, the underlying epistemological challenges span many disciplines. Internet engineering, science, economics, and public policy communities have long struggled to understand the structure and dynamics of the global Internet ecosystem. Trying to fill this gap, Internet cartography has emerged as a new field of computer as well as network science. Providing empirical grounding for this field, several research institutions execute comprehensive global Internet measurements, gathering and sharing terabytes of data annually, continuously, for years. Many companies also build proprietary databases of Internet data that help commercial endeavors and some researchers who can afford to pay access fees. However, as with so many scientific endeavors, data is not knowledge. The most sophisticated Internet mapping technology produces snapshots of limited practical utility for a range of research disciplines. For example, measurements of the interdomain routing signaling protocol (BGP) reveal logical connections between ISPs, but no insight into underlying physical router level topology. Active network-layer probing yields IP addresses along the router path between two endpoints, but mapping IP addresses to physical routers, or their operators, requires coordinated probing in combination with ancillary data sets to interpret measurements. The Internet's domain name service (DNS), which almost every Internet service relies on to map the human world of domain names (e.g., apple.com) to Internet infrastructure, relies on several assumptions about how organizations configure their naming zones, but lack of any enforcement of these assumptions generates a substantial attack surface with no associated consumer protection framework. Fundamentally, the Internet routing and naming ecosystems are both characterized by dynamics that have no rigorous theoretical foundation, and radically distributed ownership. The complexity and opacity of their behavior means that distinguishing malicious behavior from sophisticated engineering is one of the grand challenges of 21st century Internet science, technology, policy, and law enforcement. A formidable consequence has been the failure to effectively mitigate a set of pernicious and persistently unsolved security vulnerabilities fundamental to the Internet architecture, resulting in effectively unquantifiable harms due to DoS attacks, DNS and route hijacks, and other disruptive malicious activity. Our inability to even quantify the risks - which would at least enable a cyberinsurance industry to responsibly manage them - is rooted in lack of available data, and ability to soundly interpret it. We propose to develop an Open Knowledge Network (OKN) of public data on Internet structure, as manifested in the naming, addressing, and routing systems, to confront a growing empirical gap in science, security, and public communications policy. Our approach involves cutting edge Internet cartography measurement and analytic tools, crucial operational network engineering expertise required for epistemologically sound interpretations of the measurements, methodologies to synthesize different sources of data to reveal insights, and technology to responsibly manage data integrity, availability, and privacy. We structure this project in two tasks. Task 1 (Section 4.1), the central focus of the project, is a carefully structured and strategically paced 9-month team-building effort, to discover the few strategic partners who will work and contribute in Phase 2 (if approved), and other collaborators who will use the OKN and extend its reach. Our initial team members - UC San Diego, the University of Oregon's Network Resource Startup Center, NLNet Labs, and the University of Waikato - each have a well-established history of navigating the interdisciplinary challenges of Internet mapping research, including the largely proprietary nature of deployed infrastructure, and associated commercial and privacy sensitivities, especially regarding evidence of vulnerabilities or harms to businesses, consumers, and the infrastructure itself. Task 2 (Section 4.2) focuses on development necessary to most effectively explore the feasibility of the proposed OKN. We will leverage our team's decades of leadership in scientific studies and Internet measurements, recent developments in Internet cartography, and current work on analysis of the domain name resolution system (DNS) in the context of the underlying Internet topology. We have identified one immediate application of national interest to frame discussion (and Section 4.2 describes others): coupling naming (DNS) and topology data sources together to transform our understanding of the Internet and our ability to detect and attribute suspicious behavior, as well as identify macroscopic properties that reflect evidence of vulnerabilities. Our collaborators will provide additional use cases, operational networking expertise, and legal and policy analyses of data sharing incentives and constraints. Armed with a set of driving questions, we will develop a cloud-based prototype using several subsets of data (listed in Section 3) to explore the efficiency and effectiveness of cloud services as back end infrastructure for the OKN, and to estimate development effort required to support queries identified as strategic. The deliverable of this task is an experienced-based plan for an innovative, reliable, sustainable and accessible platform. We will look to the U.S. Census Bureau and the U.S. Bureau of Labor Statistics as models for our effort.
2 Appropriateness for Convergence Accelerator ProgramOpen Knowledge Network Track Relevance. This Phase 1 effort focuses on building a multidisciplinary and multi-institutional team to identify the development paths for the proposed OKN. The proposed project will take advantage of decades of U.S.-government-funded data gathered and shared by researchers to support Internet infrastructure research, and enable other substantial government-funded research that lacks the ability to gather such data itself. Our convergent approach is framed around two tasks strategically designed to accelerate uptake of the proposed knowledge network to government, industry, and academia. Our initial list of collaborators (Table ) spans a range of intellectually distinct scientific and social science disciplines that rely on Internet measurement - from network science to security to economics to law and public policy. Each research partner (UCSD, NLNet Labs, Route Views, Waikato) operates global Internet measurement infrastructures that gathers and shares large data data sets continuously. Each partner also has years of experience trying to improve transparency and risk management of the Internet ecosystem, and is well-connected in technical and policy communities that will enable us to identify additional areas of expertise that may be needed. The PIs and their research collaborators engage in both basic and applied scientific research on the Internet, ensuring that inputs and outputs of the proposed OKN will be fed by basic research and discovery. Our close working relationships with several legal scholars provide depth to our analysis and understanding of their implications. This effort also appropriately fits the RAISE program, because it targets not only multidisciplinary problems, but those where the most promising transformational advances lie at the boundaries between disciplines - legal, operational engineering, research, and government policy. The goal of tying observed properties of Internet structure to specific social, economic, or infrastructure harms will ensure that our OKN is use-inspired and application oriented. Addressing National Need. The privatization and commercialization of the Internet infrastructure has brought unimagined innovation in many dimensions, but increasingly at the expense of science, security and consumer protection, and informed public policy. Today's Internet is a haven for criminal, fraudulent, and state-based cyberoffensive behavior, and technology-based countermeasures to these threats are proving insufficient. In January 2019, the U.S. DHS issued emergency directive 19-01 , requiring all government agencies to follow five explicit best practices to protect DNS infrastructure from targeted attacks, including by nation-states [2,3]. This directive adds to the hundreds of pages of cybersecurity best practices by various U.S. agencies [4,5,6], with continued hope that industry self-regulation in this area will suffice. Yet competitive pressures clearly inhibit investment in security consumer protection measures, and the self-regulatory model of governance of Internet identifiers is failing to achieve its own standards for accountability [7,8]. Europe's recent launch of the General Data Protection Regulation has prompted ICANN's greatest challenge yet, a conflict unresolved for decades over what metadata about Internet identifier ownership should be available to whom. This meta data is a pillar in operational security efforts to combat cybercrime and other malfeasance rooted in inappropriate use of Internet names and numbers, and multiple multistakeholder processes are attempting to expeditiously sort out a compromise [9,10], although not nearly so expeditiously as the U.S. government believes is necessary . One notable stakeholder absent from these conversations is the scientific research community, both from the academic and government sector. This project will create a forum that explicitly addresses this gap, bringing together industry, government, and NGO sectors together with scientific researchers. Deliverables. We aim for our Phase 2 deliverable to be an an accurate, dynamic knowledge network based on Internet structure, and an associated interdisciplinary team of experts, which collectively offer a transformative impact on the fields of Internet science, security, and public policy, with driving use cases to identify and mitigate cybersecurity and consumer protection concerns in the largely unregulated Internet. By the end of Phase 1, we hope to have an integrated team including industry, academics, not-for-profits, and government entities. Other Phase 1 deliverables will include reports describing the workshops, lessons learned in the team-building efforts, lessons learned from our prototype experimental deployment.
3 Components to Support an Open Knowledge NetworkTo study Internet structure and phenomena, scientists rely on various platforms that continuously capture information from a broad-cross section of the Internet's topology and naming system. AS-level topology measurements rely on instrumentation operating the Border Gateway Protocol (BGP) and listening to core Internet routers around the world communicating reachability information. IP and router-level measurements rely on comprehensive measurements that actively solicit responses from routing infrastructure, and infer forward paths from these responses. DNS data associates observed network-layer behavior with the human view in the form of domain names, and its ubiquitous use means that measurement of DNS behavior reveals rich information about the evolution of the Internet and its protocols, facilitating operational and academic security research as well as law enforcement, who use DNS data to combat phishing, spam, brand and trademark infringements, and other malicious uses of domains. We review several raw and derivative data sets that are promising components of the proposed open knowledge network.
- ICANN Centralized Zone Data Service. Each Top Level Domain (TLD) registry operator maintains a zone file that contains information on each domain, including associated name server hosts, and IP addresses for those name servers. TLD zone data is inherently public via DNS queries but acquiring an entire zone file for research has historically required applying for access from each TLD registry operator, under appropriate use terms, e.g., no spamming domains. After introducing thousands of new gTLDs in 2012, ICANN established a centralized data access platform to simplify access to all zone files for those new gTLDs. (Legacy gTLDs and country-code TLDs, do not participate in this program.) Many academic researchers make use of zone files for Internet research, and even create other services based on this data. One of PI Voelker's graduate students collected, analyzed and archived changes to 1200 of these zone files every day for years  to support his own and collaborators' research [13,14].
- OpenINTEL active measurements of global namespace. NLNet labs - jointly with three other Dutch research institutions (SURFnet, SIDN Labs, and U. Twente) - has been operating the OpenINTEL project, a system for comprehensive measurements of the global DNS . OpenINTEL uses ICANN's CZDS files, and agreements with many other registries, to drive DNS queries for all covered domains once every 24 hours, covering over 216 million domains per day for: .com, .net, .org, .info, .mobi, .aero, .asia, .name, .biz, .gov, almost 1200 new gTLDs (.xxx, .xyz, .amsterdam, .berlin, ...), and many ccTLDs: .nl, .se, .nu, .ca, .fi, .at, .dk, .ru, .us. The project has collected 3 trillion data points since its inception in 2015. They have used this data to study and improve DNSSEC operational practices, DNS resilience, and identify misconfigurations.
- Border Gateway Routing Protocol (BGP) Routing data. Two organizations - U. Oregon and RIPE RIS (both collaborators) - collect and store Internet interdomain routing data from several locations around the globe [16,17]. The Network Startup Resource Center at the University of Oregon operates Route Views, the primary (NSF-funded) U.S. source of interdomain routing data for scientific research. The Route Views project was originally conceived as a tool for Internet operators to obtain real-time BGP information about how the global routing system viewed their prefixes and/or AS space, but over the years Route Views data has become invaluably critical to the Internet routing research community. In 2001, RIPE established a similar data collection capability in the Routing Information Service (RIS) . RIS data can be accessed via RIPEstat, which tries to provide a unified interface to available information about Internet number resources.
- Active traceroute measurements. CAIDA and RIPE both operate global platforms (Ark  and RIPE Atlas , respectively) which continuously probe the Internet from hundreds or thousands of vantage points (VPs). Each VP executes pre-defined network-layer measurements, e.g., pings, traceroutes, DNS queries, HTTP queries, which help researchers infer connectivity and paths. CAIDA is current hosting a RIPE NCC researcher (Stephen Strowes) for a six-month visit to collaborate on a quantitative comparison of diversity and topological coverage of measurements from the two platforms, and the epistemological impact of these differences. Since 2007, Ark has gathered 170B traceroutes (over 20 TB) from (as of May 2019) 170 Ark monitors (121 cities, 50 countries) hosted by diverse organizations: research/educational, commercial, network infrastructure, residential, etc.1 The Ark platform also performs DNS lookups of all IP addresses observed during probing.2 These datasets grow by approximately 20B traces ( ≈ 10 TB) per year. Few researchers can download data sets of this size, so we created an interactive web-based interface  to allow researchers to find the most relevant data for their research, such as all traceroutes through a given region and time period toward or across a particular address, network, or country.
- Network/organizational structure. The Internet is composed of tens of thousands of independent interconnecting autonomous systems (ASes). One organization may operate one or more autonomous systems, depending on engineering and business practices, e.g., mergers. There is no official data base mapping AS numbers to organizations owning and operating them; CAIDA maintains a heuristic-based mapping of ASes to organizations  as well as mapping of the set of IP addresses (v4 and v6) for which each network announces reachability to the global routing system .
- Curated Internet Topology Data Kits (ITDKs). Using raw traceroute and BGP data, CAIDA regularly publishes derivative data sets [34,35,36], including heavily curated two-week snapshots of raw traceroute data into Internet Topology Data Kits (ITDK) . Each ITDK contains inferred, DNS-annotated, router-level and AS-level topologies of the global Internet, based on paths gathered from a large cross-section of the global Internet. We have increased the richness of ITDKs over time by integrating new techniques as we develop them, including AS ownership inference  and scalable alias resolution (identifying which interface IP addresses belong to the same routers), which is required to convert the IP-level topology discovered by traceroute to a router-level topology . We have recently used the ITDK to develop new cartographic techniques for inferring naming (DNS) structure .
- Inferred Economic Relationships Between Networks. CAIDA operates a public web service (AS Rank) that allows exploration of routing and business relationships between ISPs (identified as ASes in the routing system) and organizations that own them . The Internet AS-level topology and its dynamics are consequences of business decisions that Internet players make, and accurate knowledge of AS business relationships is relevant to both technical and economic forces driving Internet structure and evolution. AS relationships introduce a non-trivial set of constraints on paths over which Internet traffic can flow, with implications for network robustness, traffic engineering, measurement strategies, and economic modeling of topology. Our AS relationship inference algorithm, which builds on decades of work [42,36], ranks ASes by their customer cone size, which is the number of their direct and indirect customer networks, inferred from public BGP routing data (described above).
- Security hygiene of networks. In collaboration with U. Waikato, CAIDA has developed open-source tools that enable crowd-sourced measurements of source address validation (SAV) compliance [43,44]. Source address validation is a best practice that protects other networks from spoofed denial-of-service attacks coming from one's own network. Unlike many security best practices that can be measured from anywhere on the network, measurement of SAV on a network requires attempting to transmit an invalid-source addressed packet from that network to the public Internet. We have developed client software that works on Windows, MacOS, and UNIX-like systems, periodically testing a network's ability to both send and receive packets with forged source IP addresses (spoofed packets). This platform helps operators testing their own configurations, and remediation authorities who want to prioritize SAV compliance attention where it will most benefit. We will also pursue sources of Indications of Compromise (IoCs) such as , which often include specific IP addresses and DNS hostnames, to identify and analyze networks that host systems that support the spread of malware.
4 Project Plan
4.1 Task 1: Build Team and Define OKN: People, Principles, PurposeTask 1 will focus on building the multidisciplinary and multi-institutional team needed to identify the development paths for an OKN, with the most promising scientific and economic impacts in the national interest. The goal is to develop a research plan and form a team leading to a proof-of-concept implementation. To establish strategic partnerships, we will hold weekly subgroup meetings and monthly plenary calls. We will stratify the weekly phone calls by topic, focusing different subteams on different dimensions of the OKN development: horizontal challenges; vertical challenges; strategic questions to target design of the system and interfaces; and scenario planning for different futures of Internet data governance. We will use monthly plenary calls to bring all subteams up to date on each other's progress, and offer critical feedback on ideas and results. This regular level-setting will establish extensive shared context, allowing us to focus at the workshops on hands-on design, implementation, and scenario planning and simulation exercises, described below. Each of our initial research partners (UCSD, NLNet Labs, Route Views, Waikato) has been engaged for years in gathering, curating, analyzing, and sharing data about the Internet infrastructure, including all the associated operational challenges. Our shared experience provides a basis for horizontal activities as described in the DCL. We have all struggled with balancing efficiency and power in representation formats (which ideally includes provenance and timestamp information) and storage architectures; query services; integration libraries; authentication; and user-friendly ("natural") interfaces. CAIDA's location at UC San Diego's Supercomputer Center (SDSC) will allow us to leverage SDSC's decades of expertise with big data to take these horizontal activities to more ambitious territory. Notably, the science gateways3 community led by SDSC provides an ideal model for how we can expand the scientific and collaborative opportunities of our open knowledge network across more research domains. Science gateways can also enable well-formed communities, such as CAIDA and its collaborators, to focus application development on tools and access methods rather than on the logistics of procuring, curating, and managing data and supporting systems. SDSC is pioneering this type of cyberinfrastructure; we are fortunate to have access to backend (XSEDE) resources, expertise in data restructuring to optimize performance, and usability review. Our initial industry collaborators represent those who operate root and gTLD server systems (Verisign), large scale commercial DNS infrastructure (Comcast), and an organization that uses DNS data to sell security services (Farsight). These industry experts bring critical expertise in sound interpretation of data and inferences, translation across scientific, business, and policy domains, as well as a deep understanding of incentives and constraints of community stakeholders. We will seek early in Phase 1 to find additional collaborators from industry. While some of the people in the knowledge network will be creators of knowledge, and some will be consumers of knowledge, the network also needs those who can help shape the incentives and conditions that let created knowledge be shared, i.e., allow integration of the network. That is a hard problem in this case, where there are adverse interests and stakeholders. We sought, and will continue to seek, legal and policy collaborators who have published in-depth analyses of how incentives in a competitive and litigious market have impeded Internet infrastructure security efforts, and those who have proposed solutions [47,48] and/or tracked their impact. We are also pursuing contacts in three U.S. federal agencies (FCC, FTC, and NIST) to provide their perspectives as we develop the OKN. Our academic research collaborators span networking, security, economics, and management. They will lead the vertical activities, including: collecting, taxonomizing, and prioritizing strategic queries, compiling inventories of domain-specific data, services, and ontologies; gap analysis between existing data and what is needed to satisfy queries; preparing content for the OKN, which will require extracting information from raw and curated Internet infrastructure data; and designing prototypes. For example, one goal of the prototype will be to demonstrate how integration of DNS with topology data will enable queries that can reason about suspicious activity based on patterns of IP addresses, DNS names, and their owners. Another example will be developing methodologies to join data from disparate sources to learn more about interdependencies among networks [49,50], in ways that reveal properties, e.g., resiliency or lack thereof, of individual networks and macroscopic properties of the segments of the ecosystem. To bring these communities together to design, participate in, and use a knowledge network, we will rely on existing literature on building and managing knowledge networks from MIT Sloan  and a recent NSF workshop . These descriptions conceptualize a knowledge network as an interplay between a network of people and a graph of semantic knowledge, allowing deduction of linkage among disparate entities, using data from a variety of sources. In this model, a knowledge network operates in four stages: Design, Dynamics, Behaviors, and Outcomes. The design shapes subsequent stages. Dynamics are the patterns of interactions with the real world, and feedback loops that allow behaviors to emerge. Behaviors include cohesive interaction, demonstrations of trust, connection sharing, use of a common technology platform, and investments in collaboration. These behaviors then lead to hopefully measurable outcomes. This task of the project will require not just selection of players, but also codification of the purposes of the OKN, and principles it embodies. Our approach will use the following pillars, derived from the above-referenced literature [51,52]:
- Statement of purpose, related to improved Internet security, science, and policy.
- A shared theory of change: how the knowledge network can transform scientific studies of the Internet to address national needs.
- Measurement and subsequent articulation of structured semantic knowledge in the network, including evaluating utility of existing data sets, and identifying new ones needed.
- The role of expertise and experimentation within the network to accelerate coordination and learning and cross-fertilization among industry and academics.
- Models of participation and inclusion, including how to capture created knowledge into an online knowledge structure that to some extent automates the knowledge.
- Operating models and convening structures, including workshops described below.
- Development of social norms that generate continuous improvement and incentives to participate, learning from successes of participants in their own knowledge networks.
- Developing metrics of success.
4.2 Task 2: Design KISMET prototype: Patterns, Protocols, ProductionThe most compelling initial case we have in mind is establishing DNS cartography on the solid footing that the scientific community has established for network-level cartography over the last 20 years. From a technical perspective, we are in a position to tackle (in Phase 2) the technical challenges associated with DNS cartography, and the recent investment in open DNS data and measurement provides an opportunity to demonstrate substantial impact with moderate development effort. Although we defer details of a design discussion to the workshops, we offer the following starting set of questions, based on our team's work with the data sets described in §3. These questions highlight why a knowledge graph representation is appropriate for the questions we want to ask of this data, and how such a representation will enable pursuit of new insight into Internet infrastructure.
- We will start with conceptualizing, and constructing a sample of, a heavily annotated map of the namespace, including zone creation and expiration patterns, bulk registrations, parent/child zone delegation and TTL mismatches, and shared infrastructure that represents a resilience vulnerability.
- In support of pattern detection, we will design a knowledge graph representation of the space, including analysis and visualization modules information to help detect anomalous patterns in zone files that reflect suspicious registrar or registrant behavior, e.g., orphan DNS nameservers, bulk registrations, delayed registration of domains, and new, changed, or deleted domains/nameservers. This graph should also capture concentration of domains across registrars that are also autonomous systems, such as that shown in Figure 2. Specifically, over half of observed domains in the CZDS data set on 1 May 2019 were hosted by 10 registries, mostly in the U.S. and China. Similarly, OpenIntel measurements using .com and .nl zones revealed that the vast majority of second-level domains in .com have name servers located in a single AS, while almost half of domains in the .nl zone have name servers in at least two ASes. Topological diversity is important to protect against denial-of-service attacks.
- The most powerful contribution to DNS cartography will be superimposing the DNS resolution topology knowledge graph over an underlying AS-level topology knowledge graph, revealing the ISPs hosting or providing transit for various domains and TLDs (an oversimplification depicted in Figure 3), and even the physical co-location facilities where authoritative nameservers exist. This functionality should also integrate geolocation of IPs and ASNs to reveal further patterns. This will require new visualization techniques and synthesis of graph and relational database technologies.
- Although network-topology cartography is more advanced than DNS cartography, trying to join the two graphs may require an improved ability to gather traceroute measurements from VPs co-located with BGP data. One advantage of the knowledge network will be the ability to use Route Views collectors as vantage points for traceroutes into all the neighbors peering with the collector. Doing so will require development work, specifically modification of scamper  to send packets to a supplied Route Views peer. Route Views has agreed to manage their side of this capability with their peers. This development would be transformational for the Internet measurement community: we would no longer need to send hardware to do traceroute measurements - a single Route Views collector box would allow traceroute measurements via all its peering networks, giving us co-located data plane and control (BGP) plan views by default, a challenge for over a decade.
- We will discuss how and whether to study trends in concentration of address space ownership. A May 2019 presentation by Georgia Tech researchers showed that over 60% of IPv4 address space being purchased this year was purchased by Amazon, and much of the rest was purchased by other global cloud providers . In addition, an unknown number of grey market transfers (purchasing and selling) of IPv4 address blocks  may be occurring, the study of which would require detecting anomalous changes in topology, DNS, and BGP data to infer address transfers.
- Another reasoning task will be to seek correlations of IP and DNS data and connectivity structure with blacklist and spoofing data to infer properties that seem to hinder or promote cybersecurity preparedness. As one example, we could try to compute a score for every nameserver and IP based on the likelihood that domain is machine generated, and thus more likely used for botnet command and control purposes.
- More fundamentally, the community has no scientific framework for assessing the integrity of blacklists, despite evidence for skepticism [76,77]. Access to more comprehensive DNS and routing data will enable more a rigorous approach to analyzing variations in blacklist feeds based on collection methodology, and their implications.
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Footnotes:1Map and details of Ark monitor coverage: https://www.caida.org/projects/ark. In 2012, we ported our measurement software platform to the Raspberry Pi . 2Vetted researchers can run experiments directly on the nodes [21,22,23,24,25,26,27,28,29], via an API or a web interface . 3 A science gateway  is an application-specific web-based resource for accessing HPC resources for data, software, and services that let users focus on the science, rather than the complexities of accessing unfamiliar data formats and HPC systems.
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