Safeguarding the National Broadband Map: Detecting Strategic Misreporting and Auditing Broadband Deployment via a Risk-Based Monitoring System

Abstract

The National Broadband Map (NBM) serves as the source of truth for determining location eligibility for funding programs, most notably, the Broadband Equity, Access, and Deployment (BEAD) program that allocated an unprecedented $42.45 billion that aspire to provide universal internet access in the U.S.. However, the map is built upon self-reported data from Internet Service Providers (ISPs), this creates a conflict of interest and incentive for strategic misreporting, where ISPs may "game" the system with their claims to influence funding allocation. In this work, we develop a scalable monitoring framework as the blueprint to a system that help stakeholders safeguard the NBM against inaccurate or strategic provider filings with three-part approach. First, we establishes the infrastructural foundation by developing a methodology to map ISP Provider IDs to Autonomous System Numbers (ASNs), enabling the attribution of network measurements to specific provider claims. Using four independent matching techniques based on registration data, we successfully map 72% of providers presented in the NBM to ASNs, creating the observability infrastructure necessary for measurement-based verification. Second, we provide empirical evidence that integrity failures exist at scale by investigating strategic misreporting patterns in the NBM. We develop a framework for detecting "flip-flops"—logically implausible reporting patterns where an ISP's service claim follows an A→B→A sequence across NBM releases. By filtering these events for strategic relevance based on timing, impact on BEAD eligibility, and spatial concentration, we identify more than 122,000 suspicious service claims across 25 states. These findings demonstrate that the NBM is error-prone and that existing safeguarding mechanism is insufficient. Finally, we develop a continuous, risk-based monitoring framework that uses the infrastructure and evidence from ASN to provider mapping and strategic misreporting analysis. We employ a Difference-in-Differences statistical model to establish empirical baselines for expected performance improvements in network-measurements following claimed service upgrades. By continuously monitoring crowdsourced speed test data and detecting locations that fail to demonstrate corresponding performance improvements, the framework provides a blueprint for a monitoring system that enable stakeholders to efficiently prioritize verification efforts toward the most risky claims. Taken together, we demonstrate that protecting the integrity of large-scale government programs requires systematic and continuous monitoring.