About Censys


Censys is a search engine that enables researchers to ask questions about the hosts and networks that compose the Internet. Censys collects data on hosts and websites through daily ZMap and ZGrab scans of the IPv4 address space, in turn maintaining a database of how hosts and websites are configured. Researchers can interact with this data through a search interface, report builder, and SQL engine. Details on the Censys architecture are available in our research paper.

How do I use Censys?


There is a Censys Overview for first time users. If you still have questions, feel free to reach out on the Censys Discussion.

The Censys Team


Censys started as an academic research project at the University of Michigan and Illinois Urbana-Champaign in 2015 by a team of computer scientists including: David Adrian, Michael Bailey, Zakir Durumeric, J. Alex Halderman, and Ariana Mirian.

In 2017, Censys spun-out from the University of Michigan into an independent organization founded by several of the original Censys members (Zakir, David, and Alex) along with Brian Kelly and David Corcoran. Censys is currently maintained by: David Adrian, Justin Bastress, David Corcoran, Zakir Durumeric, Chris Dzombak, Alex Halderman, and Brian Kelly. The team can be reached at team@censys.io.

Research Paper


Our research paper on Censys—A Search Engine Backed by Internet-Wide Scanning—appeared at the 22nd ACM Conference on Computer and Communications Security (CCS) in October 2015. The paper contains a full description of Censys's architecture and several use cases.


A Search Engine Backed by Internet-Wide Scanning
Zakir Durumeric, David Adrian, Ariana Mirian, Michael Bailey, J. Alex Halderman
22nd ACM Conference on Computer and Communications Security (CCS'15)

Fast Internet-wide scanning has opened new avenues for security research, ranging from uncovering widespread vulnerabilities in random number generators to tracking the evolving impact of Heartbleed. However, this technique still requires significant effort: even simple questions, such as, "What models of embedded devices prefer CBC ciphers?", require developing an application scanner, manually identifying and tagging devices, negotiating with network administrators, and responding to abuse complaints. In this paper, we introduce Censys, a public search engine and data processing facility backed by data collected from ongoing Internet-wide scans. Designed to help researchers answer security-related questions, Censys supports full-text searches on protocol banners and querying a wide range of derived fields (e.g., 443.https.cipher). It can identify specific vulnerable devices and networks and generate statistical reports on broad usage patterns and trends. Censys returns these results in sub-second time, dramatically reducing the effort of understanding the hosts that comprise the Internet. We present the search engine architecture and experimentally evaluate its performance. We also explore Censys's applications and show how recent questions become simple to answer.

Attribution


We ask that any publications that use data from Censys cite the service. If you're writing a blog post, feel free to just link to Censys. If you are writing an academic paper, please cite the following:

    @InProceedings{censys15,
        author = {Zakir Durumeric and David Adrian and Ariana Mirian and Michael Bailey and J. Alex Halderman},
        title = {A Search Engine Backed by {I}nternet-Wide Scanning},
        booktitle = {Proceedings of the 22nd ACM Conference on Computer and Communications Security},
        month = oct,
        year = 2015
    }

Requesting Additional Access


Censys is designed to help answer research questions. To prevent abuse, our rate limits and data access policies are fairly restrictive. However, we are more than happy to lift these restrictions or provide access to advanced functionality for verified researchers. Generally, we're able to meet researchers' needs by increasing API limits or providing direct SQL access to datasets on Google BigQuery.

When you contact us, please include the type of access you need, any organizational affiliation, and the types of questions you are hoping to answer.

Acknowledgements


We are extremely grateful to Google, who graciously provides much of the infrastructure that powers Censys.

We thank Michael Bailey, Matthew Bernhard, Ben Burgess, Alishah Chator, Harsha Gotur, Ariana Mirian, Drew Springall, Benjamin VanderSloot, and Eric Wustrow for their help building and maintaining Censys. We also thank Elie Bursztein, Brad Campbell, Aleksander Durumeric, James Kasten, Kyle Lady, Adam Langley, HD Moore, Pat Pannuto, Vern Paxson, Paul Pearce, Niels Provos, Drew Springall, Mark Schloesser, and the many contributors to the ZMap, ZGrab, and ZTag open source projects.

We further thank the exceptional staff at the University of Michigan and Merit Network for their help and support, including Jack Bernard, Chris Brenner, Kevin Cheek, Laura Fink, Michalis Kallitsis, Dan Maletta, Jeff Richardson, Donald Welch, and Don Winsor.

GeoLocation services are provided using the MaxMind GeoLite2 database unless specified otherwise. Routing information is provided by Merit Network and Team Cymru.

Terms of Service


By accessing or using Censys, you are agreeing to our Terms of Service.