Motivated by the impressive but diffuse scope of DDoS research and reporting, we undertake a multistakeholder (joint industry-academic) analysis to seek convergence across the best available macroscopic views of the relative trends in two dominant classes of attacks - direct-path attacks and reflection-amplification attacks. We first analyze 24 industry reports to extract trends and (in)consistencies across observations by commercial stakeholders in 2022. We then analyze ten data sets spanning industry and academic sources, across four years (2019-2023), to find and explain discrepancies based on data sources, vantage points, methods, and parameters. Our method includes a new approach: we share an aggregated list of DDoS targets with industry players who return the results of joining this list with their proprietary data sources to reveal gaps in visibility of the academic data sources. We use academic data sources to explore an industry-reported relative drop in spoofed reflection-amplification attacks in 2021-2022. Our study illustrates the value, but also the challenge, in independent validation of security-related properties of Internet infrastructure. Finally, we reflect on opportunities to facilitate greater common understanding of the DDoS landscape. We hope our results inform not only future academic and industry pursuits but also emerging policy efforts to reduce systemic Internet security vulnerabilities.

In this paper, we revisit the use of honeypots for detecting reflective amplification attacks. These measurement tools require careful design of both data collection and data analysis including cautious threshold inference. We survey common amplification honeypot platforms as well as the underlying methods to infer attack detection thresholds and to extract knowledge from the data. By systematically exploring the threshold space, we find most honeypot platforms produce comparable results despite their different configurations. Moreover, by applying data from a large-scale honeypot deployment, network telescopes, and a real-world baseline obtained from a leading DDoS mitigation provider, we question the fundamental assumption of honeypot research that convergence of observations can imply their completeness. Conclusively we derive guidance on precise, reproducible honeypot research, and present open challenges.

In this paper, we introduce a framework to observe RPKI relying parties (i.e., those that fetch RPKI data from the distributed repository) and present insights into this ecosystem for the first time. Our longitudinal study of data gathered from three RPKI certification authorities (AFRINIC, APNIC, and our own CA) identifies different deployment models of relying parties and (surprisingly) prevalent inconsistent fetching behavior that affects Internet routing robustness. Our results reveal nearly 90% of relying parties are unable to connect to delegated publication points under certain conditions, which leads to erroneous invalidation of IP prefixes and likely widespread loss of network reachability.

This paper proposes a visualization approach to address Domain Name System (DNS) security challenges, such as distributed denial of service (DDoS) and cache poisoning attacks.We present Flying Term, a new perceptually motivated visual metaphor for visualizing the dynamic nature of DNS queries. The addition of visual metaphors such as Stacking Graphs, Two Tone Pseudo Color, and Chernoff Face Glyph within the same application framework provide enhanced monitoring capability and situational awareness for visualizing DNS queries. We demonstrate our visualization's capability to help administrators identify and understand DNS querying behavior due to anomalies such as misconfiguration and security events with DNS query data acquired from a diverse set of caching servers on the Internet.