We tracked the largest volunteer security information sharing community known to date: the COVID-19 Cyber Threat Coalition, with over 4,000 members. This enabled us to address long-standing questions on threat information sharing. First, does collaboration at scale lead to better coverage? And second, does making threat data freely available improve the ability of defenders to act? We found that the CTC mostly aggregated existing industry sources of threat information. User-submitted domains often did not make it to the CTC's blocklist as a result of the high threshold posed by its automated quality assurance using VirusTotal. Although this ensured a low false positive rate, it also caused the focus of the blocklist to drift away from domains related to COVID-19 (1.4%-3.6%) to more generic abuse, such as phishing, for which established mitigation mechanisms already exist. However, in the slice of data that was related to COVID-19, we found promising evidence of the added value of a community like the CTC: just 25.1% of these domains were known to existing abuse detection infrastructures at time of listing, as compared to 58.4% of domains on the overall blocklist. From the unique experiment that the CTC represented, we draw three lessons for future threat data sharing initiatives.@en

The Domain Name System (DNS) is thought of as having the simple-sounding task of resolving domains into IP addresses. With its stub resolvers, different layers of recursive resolvers, authoritative nameservers, a multitude of query types, and DNSSEC, the DNS ecosystem is actually quite complex. In this paper, we introduce DNS Observatory: a new stream analytics platform that provides a bird's-eye view on the DNS. As the data source, we leverage a large stream of passive DNS observations produced by hundreds of globally distributed probes, acquiring a peak of 200 k DNS queries per second between recursive resolvers and authoritative nameservers. For each observed DNS transaction, we extract traffic features, aggregate them, and track the top-k DNS objects, e.g., the top authoritative nameserver IP addresses or the top domains. We analyze 1.6 trillion DNS transactions over a four month period. This allows us to characterize DNS deployments and traffic patterns, evaluate its associated infrastructure and performance, as well as gain insight into the modern additions to the DNS and related Internet protocols. We find an alarming concentration of DNS traffic: roughly half of the observed traffic is handled by only 1 k authoritative nameservers and by 10 AS operators. By evaluating the median delay of DNS queries, we find that the top 10 k nameservers have indeed a shorter response time than less popular nameservers, which is correlated with less router hops. We also study how DNS TTL adjustments can impact query volumes, anticipate upcoming changes to DNS infrastructure, and how negative caching TTLs affect the Happy Eyeballs algorithm. We find some popular domains with a a share of up to 90 % of empty DNS responses due to short negative caching TTLs. We propose actionable measures to improve uncovered DNS shortcomings.

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