BIG DATA APPROACHESTO CLOUD SECURITYPaul Morse – President, WebMall VenturesCloud Security Alliance, Seattle Chapter 3/28/...
“BIG DATA IS NOT JUST ABOUT LOTS OF DATA, IT IS ABOUT        HAVING THE ABILITY TO EXTRACT MEANING; TO SORT THROUGH THE MA...
AGENDA• Observations• Cloud Architectures/Components• Machine-Generated Data  • Sources of Data• Time Sequencing of Events...
OBSERVATIONS• Big Data solutions are changing the game for security practitioners and execs• Provide the ability to look a...
SET THE STAGE• Many perspectives to Cloud Computing• Main focus for this talk is as a Public Cloud Provider   • You are th...
YOUR CLOUD DATACENTER
SCADA                   DATA SOURCES                 Backup Generators                                           Door     ...
SECURE? THINK AGAIN.                                                     • Internet Mapping Project                       ...
CAUSE FOR PAUSE“ We hope other researchers will find the data wehave collected useful and that this publication willhelp r...
MACHINE DATA• Isn’t it really all machine data? • Machine-generated data (MGD) is the generic term for information which ...
MACHINE DATA EXAMPLESApache[Fri Sep 09 10:42:29.902022 2011] [core:error] [pid 35708:tid 4328636416] [client 72.15.99.187]...
TIME SEQUENCE OF EVENTS Outbound Traffic  Terminate Sess    Delete logs   Installer runs Upload Small File     Command    ...
TIME SEQUENCE OF EVENTS  Terminate Sess    Delete logs      Update Upload Small File    Command        Fail        Pass  L...
TIME SEQUENCE OF EVENTS  Terminate Sess    Delete logs     Update Upload Small File    Command       Fail       Pass  Logi...
SOME AREAS TO CONSIDER• Ingesting various data formats     •   Many vendors claim it is easy, when it may not be     •   T...
HACK EXAMPLES• DOJ in January   • Defacement   • What specific behavior happened and what did they do?      • Log in Remot...
VENDORS AND GETTING STARTED•   Hadoop with Flume        • Getting Started•   HP ArcSight              • Easiest – Cloud Ba...
Big Data Approaches to Cloud Security
Big Data Approaches to Cloud Security
Big Data Approaches to Cloud Security
Big Data Approaches to Cloud Security
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Big Data Approaches to Cloud Security

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Slides of a talk given to the Seattle Chapter of the Cloud Security Alliance. Looks briefly at Architectures, Sources of Log Data, and behavioral signatures in the data and issues and observations around using Big Data products for security.

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Big Data Approaches to Cloud Security

  1. 1. BIG DATA APPROACHESTO CLOUD SECURITYPaul Morse – President, WebMall VenturesCloud Security Alliance, Seattle Chapter 3/28/2013
  2. 2. “BIG DATA IS NOT JUST ABOUT LOTS OF DATA, IT IS ABOUT HAVING THE ABILITY TO EXTRACT MEANING; TO SORT THROUGH THE MASSES OF DATA ELEMENTS TO DISCOVER THE HIDDEN PATTERN, THE UNEXPECTED CORRELATION,” Art Coviello, executive chairman of RSA ON THE SURFACE, BIG DATA SEEMS TO BE ALL ABOUT BUSINESSINTELLIGENCE AND ANALYTICS, BUT IT ALSO AFFECTS THE NITTY- GRITTY OF POWER AND COOLING, NETWORKING, STORAGE AND DATA CENTER EXPANSION.
  3. 3. AGENDA• Observations• Cloud Architectures/Components• Machine-Generated Data • Sources of Data• Time Sequencing of Events• Searching for Behavior• Recent Hack Examples
  4. 4. OBSERVATIONS• Big Data solutions are changing the game for security practitioners and execs• Provide the ability to look at discovery, detection and remediation across large portions of the organization in entirely new ways• Correlation between seemingly unrelated events in near real time is now relatively easy• Growing range of solution types – simple to highly complex • Roll your own to pre-packaged solutions • On-prem, Public Cloud-based and Hybrid • Simple Log search to Predictive Analysis with complex dashboards and reporting• Some solutions have extremely short “time to value” propositions• “Big Data Washing” like “Cloud Washing” is showing up• Prices vary – Free to mondo• It is NOT the holy grail for security but has many advantages over traditional SIEM products – real time, large amounts of data, broad event correlation, etc.
  5. 5. SET THE STAGE• Many perspectives to Cloud Computing• Main focus for this talk is as a Public Cloud Provider • You are the “owner” of the facility – all of it. • Infrastructure-centric discussion• How do Big Data solutions improve Security?
  6. 6. YOUR CLOUD DATACENTER
  7. 7. SCADA DATA SOURCES Backup Generators Door Wireless Devices Backup Batteries Sensors RFID PC’s Tablets Power Card Key Storage Distribution Systems Printers Phones? This is your attack surface Temp Water SystemServers Sensors Lighting controlsRouters/Switches I want all the data in one searchable repository and available in near real time
  8. 8. SECURE? THINK AGAIN. • Internet Mapping Project • “harmless” Port ping and bot install • 660 million IPs with 71 billion ports tested • 460 Million Devices Responded • Resulted in 420 thousand bots • Stupid uid/pwd combos • Admin/admin, Admin/no pwd, root/root, root/no pwd • What’s on your network?http://internetcensus2012.bitbucket.org/paper.html
  9. 9. CAUSE FOR PAUSE“ We hope other researchers will find the data wehave collected useful and that this publication willhelp raise some awareness that, while everybody istalking about high class exploits and cyberwar, foursimple stupid default telnet passwords can give youaccess to hundreds of thousands of consumer as wellas tens of thousands of industrial devices all over theworld.”
  10. 10. MACHINE DATA• Isn’t it really all machine data? • Machine-generated data (MGD) is the generic term for information which was automatically created from a computer process, application, or other machine without the intervention of a human.• Network Device Log files• Event logs• Application logs• RFID logs• Storage logs• HVAC Logs• Sensor data• Etc.
  11. 11. MACHINE DATA EXAMPLESApache[Fri Sep 09 10:42:29.902022 2011] [core:error] [pid 35708:tid 4328636416] [client 72.15.99.187] File does not exist:/usr/local/apache2/htdocs/favicon.icoJuniperSep 10 07:06:45 host rpd[6451]: bgp_listen_accept: Connection attempt from unconfigured neighbor: 10.0.8.1+1350Sep 10 07:07:53 host login: 2 LOGIN FAILURES FROM 172.24.16.21Sep 10 07:08:25 host inetd[2785]: /usr/libexec/telnetd[7251]: exit status 0x100Oracle/SiebelSQLParseAndExecute Statement 4 0 2003-05-13 14:07:38 select ROW_ID, NEXT_SESSION, MODIFICATION_NUM from dbo.S_SSA_IDIIS192.168.114.201, -, 03/20/01, 7:55:20, W3SVC2, SALES1, 172.21.13.45, 4502, 163, 3223, 200, 0, GET, /DeptLogo.gif, -,172.16.255.255, anonymous, 03/20/01, 23:58:11, MSFTPSVC, SALES1, 172.16.255.255, 60, 275, 0, 0, 0, PASS, /Intro.htm, -,Card Reader10/23/04 06:16:32,Administrator,00000101,Anderman,Penny,00026,01000,10/22/200510/23/04 06:16:32,West Gate,00000100,Peterson,Bob,00954,01000,10/21/2005
  12. 12. TIME SEQUENCE OF EVENTS Outbound Traffic Terminate Sess Delete logs Installer runs Upload Small File Command Fail Pass Login Attempt Server TOR LB Front endIP Address/Packet T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19
  13. 13. TIME SEQUENCE OF EVENTS Terminate Sess Delete logs Update Upload Small File Command Fail Pass Login Attempt Device TOR LB Front endIP Address/Packet T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18
  14. 14. TIME SEQUENCE OF EVENTS Terminate Sess Delete logs Update Upload Small File Command Fail Pass Login Attempt DeviceIP Address/Packet T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 Door 5 Door 4 Door 3 Door 2 Door 1 T-30 T-15 T0 T15 T30 T45
  15. 15. SOME AREAS TO CONSIDER• Ingesting various data formats • Many vendors claim it is easy, when it may not be • Transforms and connectors may be required (affect performance) • Device companies create add-ons, connectors, dashboards, transforms, queries, etc • Speed of indexing determines “real time” abilities • Do you need to index ALL machine data?• Vendor-specific Query languages • No standard, some commonality • Learning curve for seriously complex queries and operationalizing environment• Dashboards and Visualizations Vary• Large number of simultaneous queries is required• Workflow is critical – what happens when you find something?• Implementation architecture – lots of hardware? Bandwidth? Security? Users?• Data Governance – You found what?
  16. 16. HACK EXAMPLES• DOJ in January • Defacement • What specific behavior happened and what did they do? • Log in Remotely • Completely replace Index.* • Solution – monitor index.* and set up a parsing stream and search for a code in the html. Call a workflow if the file changes or the code doesn’t match.• DDoS • Overwhelm Website • Solution – compare request rate of increase to a previous ‘norm”. If the disparity is great enough, call a workflow to check IP addresses of source(s). Depending on results, do nothing or script a filter or block.
  17. 17. VENDORS AND GETTING STARTED• Hadoop with Flume • Getting Started• HP ArcSight • Easiest – Cloud Based• Loggly • Sumo Logic • Splunk Storm• Logrythm • Download and Install• SumoLogic • Loggly• LogScape • Logrythm• LogStash • LogScape• Sawmill • LogStash • Sawmill• Splunk • Splunk• Splunk Storm • Hadoop/Flume/Pig

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