This document discusses Core Intel, which is part of ING's cybercrime resilience program. It aims to improve capabilities for cybercrime prevention, detection, and response. Core Intel utilizes tools like Kafka, Spark, Elasticsearch and HDFS to collect, process, analyze and store large amounts of data like network logs and threat intelligence feeds. This allows for advanced analytics, matching, alerting and monitoring to detect cyber threats. The document outlines various configurations, optimizations and best practices for operating this big data pipeline and toolset at scale.

1. Core Intel
Krzysztof Adamski, Krzysztof Żmij
On the bank secret service

2. Are security breaches common?
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/4
32412/bis-15-302-information_security_breaches_survey_2015-full-report.pdf

3. Carbanak
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https://securelist.com/blog/research/68732/the-great-bank-robbery-the-carbanak-apt/

4. Core Intel is a part of ING Cyber Crime Resilience Programme
to structurally improve the capabilities for the cybercrime
• prevention
• detection and the
• response
CoreIntel
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5. • Measures against e-banking fraud, DDoS and Advanced Persistent Threats (APTs).
• Threat intelligence allow to respond to, or even prevent, a cybercrime attack
• (This kind of intelligence is available via internal and external parties and includes both
open and closed communities)
• Monitoring, detection and response to “spear phishing”
• Detection/mitigation of infected ING systems’
• Baselining network traffic/anomaly detection
• Response to incidents (knowledge, tools, IT environment)
• Automated feeds, automated analysis and historical data analysis
The reasoning
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6. The world is not enough

7. So the challenge is…

8. Market leaders Benelux
Growth markets
Commercial Banking
Challengers
Most of our data is within Europe
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9. Market leaders Benelux
Growth markets
Commercial Banking
Challengers
but we operate globally
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10. Expect the unexpected to collect all the data
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11. • What kind of data do we need?
• Where is our data located?
• How we can potentially capture it?
• What are the legal implications?
So there is a challenge to capture „all” the data
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12. Core Intel architecture

13. So what you would like to see is…
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14. …In fact it is slightly more complicated

15. All has its own purpose. Let’s see in details.
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16. Local data collector
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17. But tell how to capture that data
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https://observer.viavisolutions.com/includes/popups/taps/tap-vs-span.php

18. Broker settings:
Replication factor >= 3
min.insync.replicas = 2
unclean.leader.election.enable = false
replica.lag.time.max.ms
Producer settings:
acks = all
retries = Integer.MAX_VALUE
max.block.ms = Long.MAX_VALUE
block.on.buffer.full = true
To have data in order
max.in.flight.requests.per.connection = 1
Kafka producer configuration (as we don’t like losing data)
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19. Central data collector
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20. Time is crucial here
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21. But your business data more, so proceed with caution
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22. • Network bandwidth control
• quota.consumer.default
• quota.producer.default
Kafka mirror maker configuration
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23. Secure data:
listeners=SSL://host.name:port
ssl.client.auth=required
ssl.keystore.location
ssl.keystore.password
ssl.key.password
ssl.truststore.location
ssl.truststore.password
Kafka mirror maker configuration
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Secure
data in
transit

24. Streaming data
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25. spark.yarn.maxAppAttempts
spark.yarn.am.attemptFailuresValidityInterval
spark.yarn.max.executor.failures
spark.yarn.executor.failuresValidityInterval
spark.task.maxFailures
spark.hadoop.fs.hdfs.impl.disable.cache
spark.streaming.backpressure.enabled=true
spark.streaming.kafka.maxRatePerPartition
Spark on yarn streaming configuration
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26. In memory data grid
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val rddFromMap = sc.fromHazelcastMap("map-name-to-be-loaded")

27. Let’s find something in these logs
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28. Matching
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Tornado - a Python web framework and asynchronous
networking library - http://www.tornadoweb.org/
MessagePack – binary transport format
http://msgpack.org/

29. • Automatically & continually match network logs <->threat intel
• When new threat intel arrives, against full history network logs
• When new network logs arrive, against full history threat intel
• Alerts are shown in a hit dashboard
• Dashboard is a web-based interfaces that provide flexible charts, querying, aggregation
and browsing
• Quality/relevance of an alert is subject to the quality of IoC feeds and completeness of
internal log data.
Hit, alerts and dashboards
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30. Be smart with your tooling
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31. and leverage e.g. elasticsearch templates
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32. Data mapping:
- doc_value
- fielddata
- fields
Cluster settings to check:
gateway.recover_after_nodes
gateway.recover_after_master_nodes
gateway.recover_after_data_nodes
indices.recovery.max_bytes_per_sec
indices.breaker.total.limit
indices.breaker.fielddata.limit
Elasticsearch configuration
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33. For those who know how to use heavy equipment
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34. Long data storage - HDFS
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35. Kafka offset management
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36. Core Intel allows users to perform advanced analytics on network logs using a set of
powerful tools
• Spark API to write code to process large data sets on a cluster
• perform complex aggregations to collect interesting statistics
• run large scale clustering algorithms with Spark’s MLLib
• run graph analyses on network logs using Spark’s GraphX
• transform and extract data for use in another system (which are better for specific analytics or
visualization purposes)
• Kafka, co you can write own Consumers and Producers to work with your data
• to perform streaming analysis on your data
• to implement your own alerting logic
• Toolset
• Programming languages: Scala, Java, Python
• IDE’s: Eclipse / Scala IDE, IPython Notebook and R Studio
Advanced analytics
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37. How do we schedule the jobs
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38. How to keep everything under control
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39. Monitoring crucial points in your data pipeline
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40. Something for smart guys
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41. Plenty of data to analyze
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42. Upcoming challenges on the operations side
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43. Shaken, not stirred?
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44. 44

45. Follow us to stay a step ahead
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