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David Henthorn [Rose-Hulman Institute of Technology] | Illuminating the Dark Data of Critical Infrastructure | InfluxDays EMEA 2021

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David Henthorn [Rose-Hulman Institute of Technology] | Illuminating the Dark Data of Critical Infrastructure | InfluxDays EMEA 2021

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Critical infrastructure, such as manufacturing facilities, power plants, dams, and chemical plants, has long experienced a phenomenon known as dark data – crucial information is locked away in disparate and proprietary systems. At Rose-Hulman Institute of Technology, we are teaching students to unlock this data with the help of tools such as Telegraf and InfluxDB to better make informed decisions. In addition, these tools allow our students to investigate best practices in data handling and critical infrastructure cybersecurity.

Critical infrastructure, such as manufacturing facilities, power plants, dams, and chemical plants, has long experienced a phenomenon known as dark data – crucial information is locked away in disparate and proprietary systems. At Rose-Hulman Institute of Technology, we are teaching students to unlock this data with the help of tools such as Telegraf and InfluxDB to better make informed decisions. In addition, these tools allow our students to investigate best practices in data handling and critical infrastructure cybersecurity.

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David Henthorn [Rose-Hulman Institute of Technology] | Illuminating the Dark Data of Critical Infrastructure | InfluxDays EMEA 2021

  1. 1. | Illuminating the Dark Data of Critical Infrastructure Deeply technical, single-track free virtual InfluxData event
  2. 2. What is Critical Infrastructure? • Dams • Power plants • Factories • Manufacturing • Transportation • Water facilities • Chemical plants • Nuclear facilities • Food and Agriculture • Healthcare United States: Cybersecurity and Infrastructure Security Agency (CISA) under Department of Homeland Security
  3. 3. Rose-Hulman Critical Infrastructure Laboratory Study modernization of control systems and how to use data to make informed engineering decisions 4
  4. 4. Critical Infrastructure often relies on Industrial Control Systems
  5. 5. Industrial Control Systems • For many years, these were designed to be “air gapped” 6
  6. 6. How do we access data? 7 +
  7. 7. DARK DATA…
  8. 8. Dark Data • Immense volumes of crucially valuable data was literally locked away with the control system • The security methodology ultimately hindered access to insight
  9. 9. How do we get our data out of our protected networks efficiently yet safely and securely? Once we have the data, how do we make informed decisions with it? So…
  10. 10. At Rose-Hulman • We use Telegraf to collect data from our control systems • We then send this data out to InfluxDB 12
  11. 11. The Purdue Model Theodore Williams Where should we do data collection?
  12. 12. Level 0/1?
  13. 13. Levels 0/1 • Devices here speak any number of industrial protocols – Modbus/TCP – EtherNet/IP – CAN bus – Profibus/Profinet – … 15 Telegraf can handle some of these!
  14. 14. But should we? • While Telegraf capable of speaking many of these protocols • Level 0/1 devices should be left to their dedicated tasks as much as possible • These devices are not updated frequently • Also, many of these protocols have NO security built into them • Telegraf agent buried deep in the network levels • Would need to manage connections to many devices 16
  15. 15. Move Up if Possible
  16. 16. What is OPC UA? • Many modern control systems are implementing OPC UA connectivity – OLE for Process Control.  MS Windows initially • DA, HA, AE, etc. – Open Platform Communications • https://opcfoundation.org – UA = unified architecture • Aggregates all the disparate sources (and protocols) into one • Built with modern internet connectivity and security in mind 18
  17. 17. Move Up if Possible
  18. 18. Move Up if Possible
  19. 19. ANOTHER OPTION: Factry.io’s Node Implementation • node-opcua-logger • Standalone program written in Node.js • Contains industry standard techniques for handling data: – Periodic scans or subscriptions – “Data compression” methods • https://github.com/coussej/node-opcua-logger
  20. 20. Henthorn Lab Telegraf OPC UA plugin • In production for ~10 months now • Uses the GOPCUA library for communication • Industry standard data compression techniques • Heartbeat techniques • Available on our Github (github.com/henthornlab)
  21. 21. Why OPC UA? 24
  22. 22. OPC UA Security and Authentication • None • Username/password • Sign • Sign and encrypt 25
  23. 23. 26
  24. 24. How do we use the data? 27
  25. 25. Two major use cases of data from critical infrastructure • What are the key performance indicators right now? • What were the key performance indicators over some time range?
  26. 26. What are the values right now? • Clients could query the OPC UA servers directly – Security and network traffic concerns here – Mission critical connections only • Clients can query the Historian for the latest values – Depending on location, there will be some latency to this – For dashboards and some webapps, not a big deal – But what about some other use cases? 29
  27. 27. Next-Gen Human Machine Interfaces 30 ABI Research
  28. 28. Telegraf exposing Prometheus-style Metrics • Telegraf’s Prometheus output plugin allows metrics to be exposed via a http/https endpoint – http://my-telegraf-host.domain/metrics • Lightweight and low latency for on-premises clients due to position in DMZ 31
  29. 29. Physical Unit Instrumentation Control System Historical data Prometheus http /metrics endpoint
  30. 30. 33 /metrics endpoint
  31. 31. Harden access to /metrics • Can easily pass that endpoint to a local or neighboring Apache or NGINX web server • These can serve to handle authentication, https, logs, load balancing, etc. • Leaves Telegraf to focus on its task 34
  32. 32. Historical Data Access 35
  33. 33. Physical Unit Instrumentation Control System Historical data Prometheus http /metrics endpoint
  34. 34. What do we do with this data? • Benchmark previous performance so we can: – Identify outliers in a currently running process – Forecast future behavior – Predict when maintenance is needed – Make informed decisions on whether to upgrade or scale up • Identify correlations and engineering trends • Aggregate data from multiple and varied sources – e.g. Anomalous electrical behavior vs. weather 37
  35. 35. Currently teaching a course on Process Analytics • Course learning objectives center on the collection and analysis of process data to make informed engineering decisions • Students typically have exposure to: – MS Excel – MATLAB – R – Python
  36. 36. Skillset Growth • Students start with familiar tools – Data into spreadsheets – CSV files • Timeseries data and databases • Move to key performance indicators (KPIs) and dashboards – InfluxDB and Grafana • Bulk of time with interactive Python data notebooks 39
  37. 37. Dashboards 40
  38. 38. Onboarding Exercise for Dashboards and Time Series Data • Loaded five-year historical data into InfluxDB for popularity of the top 100 games on Steam • Students mined the data to find KPIs and then prepare a dashboard with those KPIs • Dataset helps them understand concepts like seasonality • Quickly learn to identify outliers
  39. 39. 42
  40. 40. Jupyter Notebooks https://jupyter.o rg
  41. 41. Jupyter Notebooks • Interactive notebooks that allow engineers to mock-up a data science experiment in no time • Many are cloud-based and run through the browser, so no additional software needed • Rich support for text through Markdown language. • Includes support for mathematical equations through MathJax (subset of LaTeX) • Now supports a multitude of kernels besides Python • Easily shared and version controlled
  42. 42. 45
  43. 43. • pandas dataframe filled with historized data • Visualization techniques • Dimensionality reduction • k-means clustering • Principal Component Analysis • Time series forecasting • Regression techniques 46
  44. 44. Notebooks: Focus is on communications • Clear are reproducible connections to data • Processing techniques with lots of comments • Crisp, informative visuals 47
  45. 45. Conclusions: • We are working to create secure channels to bring data out of critical infrastructure • Once out, we want reproducible data and methods • Data stack: Equipment  OPC UA  Telegraf  InfluxDB • Methods: Grafana, Jupyter Notebooks 48
  46. 46. Questions??

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