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Monday, April 20 • 4:00pm - 5:00pm
Lessons Learned from Building and Running MHN, the World's Largest Crowd-sourced Honeynet

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Honeypots are really useful for collecting security data for research, especially around botnets, scanning hosts, password brute forcers, and other misbehaving systems. They are also the cheapest way collect this data at scale. Deploying many types of honeypots across geo-diverse locations of the Internet improves the aggregate data quality and provides a holistic view. This provides insight into both global trends of attacks and network activity as well as the behaviors of individual malicious systems. For these reasons, we started the Modern Honey Network, which is both an open source (GPLv3) project and a community of hundreds of MHN servers that manage and aggregate data from thousands of heterogeneous honeypots (Dionaea, Kippo, Amun, Conpot, Wordpot, Shockpot, and Glastopf) and network sensors (Snort, Suricata, p0f) deployed by different individuals and organizations as a distributed sensor network. The project has turned into the largest crowdsourced honeynet in the world consisting of thousands of diverse sensors deployed across 35 countries and 5 continents worldwide. Sensors are operated by all sorts of people from hobbyists, to academic researchers, to Fortune 1000 companies. In this talk we will discuss our experience in starting this project, analyzing the data, and building a crowdsourced global sensor network for tracking security threats and gathering interesting data for research. We've found that lots of people like honeypots, especially if you give them a cool realtime visualization of their data and make it easy to setup; lots of organizations will share their data with you if it is part of a community; and lots of companies will deploy honeypots as additional network sensors, especially if you make it easy to deploy/manage/integrate with their existing security tools.

avatar for Jason Trost

Jason Trost

VP of Threat Research, Anomali, Inc.
Jason Trost is the VP of Threat Research at Anomali, Inc. and leads Anomali Labs, the research team. He has worked in security for more than ten years, and he has several years of experience leveraging big data technologies for security data mining and analytics. He is deeply interested in network security, DFIR, honeypots, big data and machine learning. He is currently focused on building highly scalable systems for processing, analyzing, and... Read More →

Monday April 20, 2015 4:00pm - 5:00pm

Attendees (22)