Data Driven Web Application Security

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Data Driven Web Application Security Mike Arpaia Kyle Barry

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Mike Arpaia Senior Software Engineer @mikearpaia

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Mike Arpaia Senior Software Engineer @mikearpaia Kyle Barry Security Engineering Manager @allofmywats

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https://www.etsy.com/listing/92868829/the-oh-my-orange-elephant-designer-wall

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https://www.etsy.com/listing/116016218/atomic-orbits-chemistry-fat-quarter

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https://www.etsy.com/listing/104411356/leather-iphone-44s-case-slipcover-sleeve

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Data Infrastructure

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https://github.com/etsy/dashboard

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https://github.com/etsy/statsd

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if is_xss($request_params) { StatsD::increment('security.potenital_xss'); }

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Splunk

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MySQL

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Sharded application data http://www.slideshare.net/jgoulah/the-etsy-shard-architecture-starts-with-s-and-ends-with-hard

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Dozens of database servers

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Hundreds of tables

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Postgres

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Legacy

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Hadoop

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MapReduce

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Disk Performance 0 500 1000 1500 2000 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Capacity in GB

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Disk Performance 0 500 1000 1500 2000 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Capacity in GB Transfer Rate in GB/s

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Let’s add disks! 0 275 550 825 1100 1 2 3 4 5 6 7 8 9 10 Seconds it takes to read 1 TB of data at 1 GB/s

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Sounds good.

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Good for ad-hoc, whole dataset analysis Linearly scalable programming model

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MySQL data

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Event logs & Visit logs

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Cascading

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Complex Workﬂows

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Less lines of code

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Minimal barrier to entry

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Awesome Data Team

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96 cores

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384 GB of RAM

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24 TB of storage

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...per 2U of rack space

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160 nodes 960 TB storage 3840 cores 15 TB of RAM

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Vertica

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Proprietary

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Columnar

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Postgres-like syntax

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MySQL + Postgres

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Fast analytics

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Security Mechanisms

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First, a thesis

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The security posture of your application is directly proportional to how much you know about your application.

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Reactive Security

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Real-time event monitoring and alerting Events that trigger immediate response You always query the same data and you do it often

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Proactive Security

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Things we do now to protect us later Actions taken to prevent future compromise

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Incident Response

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Ad-hoc analysis of a large dataset Driven by an event or incident You’re not going to do it more than once Needs to be fast

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Gather data to create reactive security mechanisms Gather data to create proactive security mechanisms Directly create a new proactive security mechanism Perform incident response

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Gather data to create reactive security mechanisms Gather data to create proactive security mechanisms Directly create a new proactive security mechanism Perform incident response

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Gather data to create reactive security mechanisms Gather data to create proactive security mechanisms Directly create new proactive security mechanisms Perform incident response

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Gather data to create reactive security mechanisms Gather data to create proactive security mechanisms Directly create new proactive security mechanisms Perform incident response

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Case Studies

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Reactive Security

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Alerting

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Use analytics to set thresholds

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Reporting

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SuperBIT

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Putting it together

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Proactive Security

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Goal Full-site SSL for all Etsy sellers

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analytics_cascade do analytics_flow do analytics_source 'event_logs' tap_db_snapshot 'users_index' assembly 'event_logs' do group_by 'user_id', 'scheme' do count 'value' end end assembly 'users_index' do project 'user_id', 'is_seller' end assembly 'ssl_traffic' do project 'user_id', 'is_seller', 'scheme', 'value' group_by 'is_seller', 'scheme' do count 'value' end end analytics_sink 'ssl_traffic' end end

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Keeping current

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Two Factor Authentication

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Do Etsy app users use two factor auth?

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Splunk & Vertica

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Proactively Realtime

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Content Security Policy Violations

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Incident Response

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Needle in a haystack

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• URL Patterns • IP Addresses Simple Patterns

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analytics_cascade do analytics_flow do analytics_source 'access_logs' assembly 'incident_response' do query_event 'timestamp', 'request_uri', 'useragent', 'ip' where '"/bad_url.php'".equals(request_uri:string) group_by ’url’ do count 'value' end end analytics_sink 'incident_response' end end

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Phishing Attack In Two Parts

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Part One

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Part Two

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source=”access_logs” client_ip=10.163.2.3 | transaction request_uri

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Collusion Fraud

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Look for patterns Incident Response

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Set up monitoring Be reactive

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Stay Aware Get proactive

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Conclusions

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Instrument your application at length Understand security mechanisms Use your data and use it often

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