Big Data Visualization
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Raffael Marty gave a presentation on big data visualization. He discussed using visualization to discover patterns in large datasets and presenting security information on dashboards. Effective dashboards provide context, highlight important comparisons and metrics, and use aesthetically pleasing designs. Integration with security information management systems requires parsing and formatting data and providing interfaces for querying and analysis. Marty is working on tools for big data analytics, custom visualization workflows, and hunting for anomalies. He invited attendees to join an online community for discussing security visualization.
Big Data Visualization
- 1. Raffael Marty, CEO Big Data Visualization London February, 2015
- 2. Security. Analytics. Insight.2 • Visualization • Design Principles • Dashboards • SOC Dashboard • Data Discovery and Exploration • Data Requirements for Visualization • Big Data Lake Overview
- 3. Security. Analytics. Insight.3 I am Raffy - I do Viz! IBM Research
- 5. Security. Analytics. Insight.5 Why Visualization? the stats ... http://en.wikipedia.org/wiki/Anscombe%27s_quartet the data...
- 6. Security. Analytics. Insight.6 Why Visualization? http://en.wikipedia.org/wiki/Anscombe%27s_quartet Human analyst: • patterndetection • remembers context • fantasticintuition • canpredict
- 7. Security. Analytics. Insight.7 Visualization To … Present / Communicate Discover / Explore
- 9. Security. Analytics. Insight.9 Choosing Visualizations Objective AudienceData
- 10. Security. Analytics. Insight.10 • Objective: Find attackers in the network moving laterally • Defines data needed (netflow, sflow, …) • maybe restrict to a network segment • Audience: security analyst, risk team, … • Informs how to visualize / present data For Example - Lateral Movement Recon Weaponize Deliver Exploit Install C2 Act
- 11. Security. Analytics. Insight.11 • Show comparisons, contrasts, differences • Show causality, mechanism, explanation, systematic structure. • Show multivariate data; that is, show more than 1 or 2 variables. by Edward Tufte Principals of Analytic Design
- 12. Security. Analytics. Insight.12 Show Context 42
- 13. Security. Analytics. Insight. 42 is just a number and means nothing without context 13 Show Context
- 15. Security. Analytics. Insight.15 Use Numbers To Highlight Most Important Parts of Data Numbers Summaries
- 16. Security. Analytics. Insight.16 Additional information about objects, such as: • machine • roles • criticality • location • owner • … • user • roles • office location • … Add Context source destination machine and user context machine role user role
- 17. Security. Analytics. Insight.17 Traffic Flow Analysis With Context
- 18. Security. Analytics. Insight.18 http://www.scifiinterfaces.com/ • Black background • Blue or green colors • Glow Aesthetics Matter
- 19. Security. Analytics. Insight.19 B O R I N G
- 21. Security. Analytics. Insight.21 • Audience, audience, audience! • Comprehensive Information (enough context) • Highlight important data • Use graphics when appropriate • Good choice of graphics and design • Aesthetically pleasing • Enough information to decide if action is necessary • No scrolling • Real-time vs. batch? (Refresh-rates) • Clear organization Dashboard Design Principles
- 23. Security. Analytics. Insight.23 Mostly Blank
- 24. Security. Analytics. Insight.24 • Disappears too quickly • Analysts focus is on their own screens • SOC dashboard just distracts • Detailed information not legible • Put the detailed dashboards on the analysts screens! Dashboards For Discovery
- 25. Security. Analytics. Insight.25 • Provide analyst with context • “What else is going on in the environment right now?” • Bring Into Focus • Turn something benign into something interesting • Disprove • Turn something interesting into something benign Use SOC Dashboard For Context Environment informs detection policies
- 26. Security. Analytics. Insight.26 Show Comparisons Current Measure week prior
- 27. Security. Analytics. Insight.27 • News feed summary (FS ISAC feeds, mailinglists, threat feeds) • Monitoring twitter or IRC for certain activity / keywords • Volumes or metrics (e.g., #firewall blocks, #IDS alerts, #failed transactions) • Top N metrics: • Top 10 suspicious users • Top 10 servers connecting outbound What To Put on Screens Provide context to individual security alerts http://raffy.ch/blog/2015/01/15/dashboards-in-the-security-opartions-center-soc/
- 29. Security. Analytics. Insight.29 Visualize Me Lots (>1TB) of Data
- 30. Security. Analytics. Insight.30 Information Visualization Mantra Overview Zoom / Filter Details on Demand Principle by Ben Shneiderman • summary / aggregation • data mining • signal detection (IDS, behavioral, etc.)
- 31. Security. Analytics. Insight.31 • Access to data • Parsed data and data context • Data architecture for central data access and fast queries • Application of data mining (how?, what?, scalable, …) • Visualization tools that support • Complex visual types (||-coordinates, treemaps, heat maps, link graphs) • Linked views • Data mining (clustering, …) • Collaboration, information sharing • Visual analytics workflow Visualization Challenges
- 32. Big Data Lake
- 33. Security. Analytics. Insight.33 • One central location to store all cyber security data • “Data collected only once and third party software leveraging it” • Scalability and interoperability • More than deploying an off the shelf product from a vendor • Data use influences both data formats and technologies to store the data • search, analytics, relationships, and distributed processing • correlation, and statistical summarization • What to do with Context? Enrich or join? • Hard problems: • Parsing: can you re-parse? Common naming scheme! • Data store capabilities (search, analytics, distributed processing, etc.) • Access to data: SQL (even in Hadoop context), how can products access the data? The Big Data Lake
- 34. Security. Analytics. Insight.34 Federated Data Access SIEM dispatcher SIEM connector SIEM console Prod A AD / LDAP HR … IDS FW Prod B DBs Data Lake Caveats: • Dispatcher? • Standard access to dispatcher / products enabled • Data lake technology? SNMP
- 35. Security. Analytics. Insight.35 Multiple Data Stores raw logs key-value structured real-time processing (un)-structured data context SQL s t o r a g e stats index queue distributed processing a c c e s s graph Caveat: • Need multiple types of data stores
- 36. Security. Analytics. Insight.36 Technologies (Example) raw logs key-value (Cassandra) columnar (parquet) real-time processing (Spark) (un)-structured data context SQL (Impala, SparkSQL) H D F S aggregates index (ES) queue (Kafka) distributed processing (Spark) a c c e s s graph (GraphX) Caveat: • No out of the box solution available
- 37. Security. Analytics. Insight.37 SIEM Integration - Log Management First SIEM columnar or search engine or log management processing SIEM connector raw logs SIEM console SQL or search interface processing filtering H D F S e.g., PIG parsing
- 38. Security. Analytics. Insight.38 Simple SIEM Integration raw, csv, json flume log data SQL (Impala, with SerDe) H D F S SIEM connector SIEM Requirement: • SIEM connector to forward text- based data to Flume. SQL interface Tableau, etc. SIEM console
- 39. Security. Analytics. Insight.39 SIEM Integration - Advanced SIEM columnar (parquet) processing syslog data SQL (Impala, SparkSQL) H D F S index (ES) queue (Kafka) a c c e s s other data sources SIEM connector raw logs SIEM console SQL and search interface Tableau, Kibana, etc. requires parsing and formatting in a SIEM readable format (e.g., CEF)
- 40. Security. Analytics. Insight.40 What I am Working On Data Stores Analytics Forensics Models Admin 10.9.79.109 --> 3.16.204.150 10.8.24.80 --> 192.168.148.193 10.8.50.85 --> 192.168.148.193 10.8.48.128 --> 192.168.148.193 10.9.79.6 --> 192.168.148.193 10.9.79.6 10.8.48.128 80 53 8.8.8.8 127.0.0.1 Anomalies Decomposition Data Seasonal Trend Anomaly Details “Hunt” ExplainVisual Search • Big data backend • Own visualization engine (Web-based) • Visualization workflows
- 41. Security. Analytics. Insight.41 BlackHat Workshop Visual Analytics - Delivering Actionable Security Intelligence August 1-6 2015, Las Vegas, USA big data | analytics | visualization
- 42. Security. Analytics. Insight.42 http://secviz.org List: secviz.org/mailinglist Twitter: @secviz Share, discuss, challenge, and learn about security visualization. Security Visualization Community
- 43. Security. Analytics. Insight. raffael.marty@pixlcloud.com http://slideshare.net/zrlram http://secviz.org and @secviz Further resources: