Open Security Operations Center - OpenSOC
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The document introduces the Open Security Operations Center (OpenSOC), designed to analyze over 1.2 million network packets per second in real-time, aimed at minimizing the damage from potential security breaches. It discusses the project's evolution, architecture, capabilities, and deployment details, highlighting its use of big data technologies like Hadoop and Kafka for effective telemetry analysis. Best practices, lessons learned during development and deployment, and community collaboration opportunities are also emphasized.
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{!
“msg_key1”: “msg value1”,!
“src_ip”: “10.20.30.40”,!
“dest_ip”: “20.30.40.50”,!
“domain”: “mydomain.com”!
}!
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whois:[ {!
OrgId:CISCOS,!
Parent:NET-144-0-0-0-0,!
OrgAbuseName:Cisco Systems Inc,!
RegDate:1991-01-171991-01-17,!
OrgName:Cisco Systems,!
Address:170 West Tasman Drive,!
NetType:Direct Assignment!
} ],!
“cif”:”Yes”!
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geo:[ {region:CA,!
postalCode:95134,!
areaCode:408,!
metroCode:807,!
longitude:-121.946,!
latitude:37.425,!
locId:4522,!
city:San Jose,!
country:US!
}]!
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Open Security Operations Center - OpenSOC
- 1. OpenSOC The Open Security Operations Center for Analyzing 1.2 Million Network Packets per Second in Real Time James Sirota, Big Data Architect Cisco Security Solutions Practice jsirota@cisco.com Sheetal Dolas Principal Architect Hortonworks sheetal@hortonworks.com June 3, 2014
- 2. 2 Over Next Few Minutes § Problem Statement Business Case for OpenSOC § Solution Architecture and Design § Best Practices and Lessons Learned § Q A
- 4. “There's now a growing sense of fatalism: It's no longer if or when you get hacked, but the assumption is that you've already 4 been hacked, with a focus on minimizing the damage.” Source: Dark Reading / Security’s New Reality: Assume The Worst
- 5. 5 Breaches Happen in Hours… But Go Undetected for Months or Even Years Source: 2013 Data Breach Investigations Report Seconds Minutes Hours Days Weeks Months Years Initial Attack to Initial Compromise 10% 75% 12% 2% 0% 1% 1% Initial Compromise to Data Exfiltration 8% 38% 14% 25% 8% 8% 0% Initial Compromise to Discovery 0% 0% 2% 13% 29% 54% 2% Discovery to Containment/ Restoration 0% 1% 9% 32% 38% 17% 4% Timespan of events by percent of breaches In 60% of breaches, data is stolen in hours 54% of breaches are not discovered for months
- 6. 6 Cisco Global Cloud Index Source: 2014 Cisco Global Cloud Index
- 7. 7 Introducing OpenSOC Intersection of Big Data and Security Analytics Scalable Compute Multi Petabyte Storage Interactive Query Real-Time Search Unstructured Data Scalable Stream Processing Data Access Control OpenSOC Real-Time Alerts Anomaly Detection Data Correlation Rules and Reports Predictive Modeling UI and Applications Big Data Platform Hadoop Elastic Search
- 8. 8 OpenSOC Journey Sept 2013 First Prototype Dec 2013 Hortonworks joins the project March 2014 Platform development finished Sept 2014 General Availability May 2014 CR Work off April 2014 First beta test at customer site
- 9. 9 Solution Architecture Design
- 10. 10 OpenSOC Conceptual Architecture Raw Network Stream Network Metadata Stream Netflow Syslog Raw Application Logs Other Streaming Telemetry Applications + Analyst Tools HBase Hive Raw Packet Store Long-Term Store Elastic Search Real-Time Index Network Packet Mining and PCAP Reconstruction Log Mining and Analytics Big Data Exploration, Predictive Modeling Threat Intelligence Parse + Format Enrich Alert Feeds Enrichment Data
- 11. 11 Key Functional Capabilities § Raw Network Packet Capture, Store, Traffic Reconstruction § Telemetry Ingest, Enrichment and Real-Time Rules-Based Alerts § Real-Time Telemetry Search and Cross-Telemetry Matching § Automated Reports, Anomaly Detection and Anomaly Alerts § Rich Analytics Apps and Integration with Existing Analytics Tools
- 12. 12 The OpenSOC Advantage § Fully-Backed by Cisco and Used Internally for Multiple Customers § Free, Open Source and Apache Licensed § Built on Highly-Scalable and Proven Platforms (Hadoop, Kafka, Storm) § Extensible and Pluggable Design § Flexible Deployment Model (On-Premise or Cloud) § Centralize your processes, people and data
- 13. 13 OpenSOC Deployment at Cisco Hardware footprint (40u) § 14 Data Nodes (UCS C240 M3) § 3 Cluster Control Nodes (UCS C220 M3) § 2 ESX Hypervisor Hosts (UCS C220 M3) § 1 PCAP Processor (UCS C220 M3 + Napatech NIC) § 2 SourceFire Threat alert processors § 1 Anue Network Traffic splitter § 1 Router § 1 48 Port 10GE Switch Software Stack § HDP 2.1 § Kafka 0.8 § Elastic Search 1.1 § MySQL 5.5
- 14. 14 OpenSOC - Stitching Things Together Source Systems Data Collection Messaging System Real Time Processing Storage Access Kafka Storm B Topic N Topic Elastic Search Index Web Services Search PCAP Reconstruction HBase PCAP Table Analytic Tools R / Python Power Pivot Tableau Hive Raw Data ORC Passive Tap PCAP Topic DPI Topic A Topic Telemetry Sources Syslog HTTP File System Other Flume Agent A Agent B Agent N A Topology B Topology N Topology PCAP Traffic Replicator PCAP Topology DPI Topology
- 15. 15 OpenSOC - Stitching Things Together Source Systems Data Collection Messaging System Real Time Processing Storage Access Kafka Storm B Topic N Topic Elastic Search Index Web Services Search PCAP Reconstruction HBase PCAP Table Analytic Tools R / Python Power Pivot Tableau Hive Raw Data ORC Passive Tap PCAP Topic DPI Topic A Topic Telemetry Sources Syslog HTTP File System Other Flume Agent A Agent B Agent N PCAP Topology DPI Topology A Topology B Topology N Topology PCAP Traffic Replicator Deeper Look
- 16. 16 PCAP Topology Real Time Processing Storage Storm Hive Raw Data ORC Elastic Search Index HBase PCAP Table Kafka Spout Parser Bolt HDFS Bolt ES Bolt HBase Bolt
- 17. 17 DPI Topology Telemetry Enrichment Real Time Processing Storage Storm Hive Raw Data ORC Elastic Search Index HBase PCAP Table Kafka Spout Parser Bolt GEO Enrich Whois Enrich CIF Enrich HDFS Bolt ES Bolt
- 18. 18 Enrichments {! “msg_key1”: “msg value1”,! “src_ip”: “10.20.30.40”,! “dest_ip”: “20.30.40.50”,! “domain”: “mydomain.com”! }! Parser Bolt GEO Enrich RAW Message whois:[ {! OrgId:CISCOS,! Parent:NET-144-0-0-0-0,! OrgAbuseName:Cisco Systems Inc,! RegDate:1991-01-171991-01-17,! OrgName:Cisco Systems,! Address:170 West Tasman Drive,! NetType:Direct Assignment! } ],! “cif”:”Yes”! Who Is Enrich geo:[ {region:CA,! postalCode:95134,! areaCode:408,! metroCode:807,! longitude:-121.946,! latitude:37.425,! locId:4522,! city:San Jose,! country:US! }]! CIF Enrich Enriched Message Cache MySQL Geo Lite Data Cache HBase Who Is Data Cache HBase CIF Data
- 19. 19 Applications: Telemetry Matching and DPI Step1: Search Step2: Match Step3: Analyze Step4: Build PCAP
- 20. 20 Integration with Analytics Tools Dashboards Reports
- 21. 21 Best Practices and Lessons Learned
- 22. 22 Journey Towards Highly Scalable Application
- 23. 23 Kafka Tuning
- 24. 24 This is where we began
- 25. 25 Some code optimizations and increased parallelism
- 26. 26 Kafka Tuning § Is Disk I/O heavy § Kafka 0.8+ supports replication and JBOD § Better performance compared to RAID § Parallelism is largely driven by number of disks and partitions per topic § Key configuration parameters: § num.io.threads - Keep it at least equal to number of disks provided to Kafka § num.network.threads - adjust it based on number of concurrent producers, consumers and replication factor
- 27. 27 After Kafka Tuning
- 28. 28 Bottleneck Isolation, Resource Profiling, Load Balancing
- 29. 29 HBase Tuning
- 30. 30 This is where we began
- 31. 31 Row Key Design § Row Key design is critical (gets or scans or both?) § Keys with IP Addresses § Standard IP addresses have only two variations of the first character : 1 2 § Minimum key length will be 7 characters and max 15 with a typical average of 12 § Subnet range scans become difficult – range of 90 to 220 excludes 112 § IP converted to hex (10.20.30.40 = 0a141e28) § gives 16 variations of first key character § consistently 8 character key § Easy to search for subnet ranges
- 32. 32 Experiments with Row Key
- 33. 33 Region Splits § Know your data § Auto split under high workload can result into hotspots and split storms § Understand your data and presplit the regions § Identify how many regions a RS can have to perform optimally. Use the formula below (RS memory)*(total memstore fraction)/((memstore size)*(# column families))!
- 34. 34 With Region Pre-Splits
- 35. 35 Know Your Application § Enable Micro Batching (client side buffer) § Smart shuffle/grouping in storm § Understand your data and situationally exploit various WAL options § Watch for many minor compactions § For heavy ‘write’ workload Increase hbase.hstore.blockingStoreFiles (we used 200)
- 36. 36 And Finally
- 37. 37 Kafka Spout
- 38. 38 Kafka Spout § Parallelism is controlled by number of partitions per topic § Set Kafka spout parallelism equal to number of partitions in topic § Other key parameters that drive performance § fetchSizeBytes! § bufferSizeBytes!
- 39. 39 Mysteriously Missing Data
- 40. 40 Mysteriously Missing Data Root Cause § A bug in Kafka spout that used to miss out some partitions and loose data § It is now fixed and available from Hortonworks repository ( http://repo.hortonworks.com/content/repositories/releases/org/apache/ storm/storm-Kafka )
- 41. 41 Storm
- 42. 42 Storm § Every small thing counts at scale § Even simple string operations can slowdown throughput when executed on millions of Tuples
- 43. 43 Storm § Error handling is critical § Poorly handled errors can lead to topology failure and eventually loss of data (or data duplication)
- 44. 44 Storm § Tune Scale individual spout and bolts before performance testing/tuning entire topology § Write your own simple data generator spouts and no-op bolts § Making as many things configurable as possible helps a lot
- 45. 45 Lessons Learned § When it comes to Hadoop…partner up § Separate the hype from the opportunity § Start small then scale up § Design Iteratively § It doesn’t work unless you have proven it at scale § Keep an eye on ROI
- 46. 46 Looking for Community Partners Cisco + Hortonworks + Community Support for OpenSOC How can you contribute? § Technology Partner Program – contribute developers to join the Cisco and Hortonworks team
- 47. Thank you! We are hiring: jsirota@cisco.com sheetal@hortonworks.com