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Process Mining Organization Email Data and National Security Implications

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Unifying Themes in Complex Systems X (ICCS 2020)

Abstract

Many think of processes as sequential, deliberate activities which sustain businesses and government agencies; employees integrate themselves into defined organizational processes. From an ecosystem vantage, however, emergent processes exist and are discoverable. Emergent ecosystems form without human intention and may be especially influenceable. If emergent organizational processes–especially critical infrastructure processes–were explicit, they may be exploited. Tremendous intelligence is contained within semi-structured and unstructured organizational data sources. Properly analyzed, these data provide government and private organizations with actionable management and risk mitigation insights. Using explainable process technologies combined with natural language processing, a private critical infrastructure participant’s organizational process model is discovered from semi-structured email data. Data derived from the process model are presented which elucidate internal operations and contribute to automated situational awareness of dynamically evolving events. National security implications and future research needs are described.

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References

  1. Prigogine, I., Nicolis, G., Babloyantz, A.: Thermodynamics of evolution. Phys. Today 25(11), 23–28 (1972). https://doi.org/10.1063/1.3071090

    Article  Google Scholar 

  2. Hidalgo, C.: Why Information Grows: The Evolution of Order, from Atoms to Economies. Basic Books (2015)

    Google Scholar 

  3. Whitehead, A.N.: Process and Reality, 2nd edn. Free Press, New York (1979)

    Google Scholar 

  4. Bicknell, J.W.: Process mining technologies. ORMS Today 46(5) (2019). https://doi.org/10.1287/orms.2019.05.01

  5. IEEE: IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. IEEE Std 1849-2016, pp. 1–50, Nov 2016. https://doi.org/10.1109/IEEESTD.2016.7740858

  6. van der Aalst, W.M.P., Nikolov, A.: EMailAnalyzer : an e-mail mining plug-in for the ProM framework (2007)

    Google Scholar 

  7. Jlailaty, D., Grigori, D., Belhajjame, K.: A framework for mining process models from emails logs. ArXiv. abs/1609.06127 (2016)

    Google Scholar 

  8. Allard, T., Alvino, P., Shing, L., Wollaber, A., Yuen, J.: A dataset to facilitate automated workflow analysis. PLoS ONE 14(2), e0211486 (2019). https://doi.org/10.1371/journal.pone.0211486

    Article  Google Scholar 

  9. Bicknell, J.W., Krebs, W.G.: Methods and systems for estimating process capacity. United States 10,846,194, issued November 24, (2020)

    Google Scholar 

  10. Bicknell, J.W., Krebs, W.G.: Methods and systems for inferring behavior and vulnerabilities from process models. U.S. Patent Application No. 16/440,639. Washington, DC: U.S. Patent and Trademark Office

    Google Scholar 

  11. Enron Email Dataset, 18 May 2015. https://www.cs.cmu.edu/~./enron/. Accessed 12 Mar 2019

  12. van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn, 2016 edn. Springer, New York, NY (2016)

    Google Scholar 

  13. Bicknell, J.W., Krebs, W.G.: Detecting botnet signals using process mining (2019)

    Google Scholar 

  14. Khinchin, A.Y.: Mathematical Foundations of Information Theory, 1st Dover edn. Dover Publications, Mineola, NY (1957)

    Google Scholar 

  15. Kemeny, J.G., Snell, J.L.: Finite Markov Chains: With a New Appendix “Generalization of a Fundamental Matrix.” Springer, New York (1976)

    MATH  Google Scholar 

  16. Belluzzo, T.: A framework for discrete-time Markov chains analysis (2019)

    Google Scholar 

  17. Feldman, J.F., Roberge, F.A.: The normalized transition matrix. A method for the measure of dependence between inter-spike intervals. Electroencephalogr. Clin. Neurophysiol. 30(1), 87–90 (1971). https://doi.org/10.1016/0013-4694(71)90209-4

    Article  Google Scholar 

  18. Hanks, E.M., Hooten, M.B., Alldredge, M.W.: Continuous-time discrete-space models for animal movement. Ann. Appl. Stat. 9(1), 145–165 (2015). https://doi.org/10.1214/14-AOAS803

    Article  MathSciNet  MATH  Google Scholar 

  19. Bielecki, T.R., Cialenco, I., Gong, R., Huang, Y.: Wiener-Hopf factorization for time-inhomogeneous Markov chains and its application (2018). https://arxiv.org/abs/1801.05553. Accessed 02 Oct 2019 [Online]

  20. McLean, B., Elkind, P.: The Smartest Guys in the Room: The Amazing Rise and Scandalous Fall of Enron, Reprint Portfolio Trade, New York (2004)

    Google Scholar 

  21. Bag-of-words model. Wikipedia, 07 Sep 2019. https://en.wikipedia.org/w/index.php?title=Bag-of-words_model&oldid=914476224. Accessed 18 Sep 2019 [Online]

  22. Krebs, W.G., Alexandrov, V., Wilson, C.A., Echols, N., Yu, H., Gerstein, M.: Normal mode analysis of macromolecular motions in a database framework: Developing mode concentration as a useful classifying statistic. Proteins Struct. Funct. Bioinforma. 48(4), 682–695 (2002). https://doi.org/10.1002/prot.10168

    Article  Google Scholar 

  23. Normal mode. Wikipedia, 08 Sep 2019. https://en.wikipedia.org/w/index.php?title=Normal_mode&oldid=914576448. Accessed 18 Sep 2019 [Online]

  24. Bicknell, J.W., Krebs, W.G.: Process mining: the missing piece in information warfare. ResearchGate (2019). https://doi.org/10.13140/RG.2.2.23584.94722/1

    Article  Google Scholar 

  25. Bicknell, J.W., Krebs, W.G.: FOCAL information warfare defense standard. ResearchGate, June 2019. https://doi.org/10.13140/RG.2.2.12672.07687.

  26. Sviridova, A.: Vectors of the development of military strategy, 04 Mar 2019

    Google Scholar 

  27. Mueller, R.S.: Report on the investigation into Russian interference in the 2016 presidential election, Mar 2019. https://www.hsdl.org/?abstract&did=824221. Accessed 01 Oct 2019 [Online]

  28. Chotikul, D.: The Soviet Theory of Reflexive Control in Historical and Psychocultural Perspective: Preliminary Study. Naval Postgraduate School, Monterey, California (1986)

    Book  Google Scholar 

  29. Novikov, D.A., Chkhartishvili, A.G.: Reflexion and Control: Mathematical Models. CRC Press (2014)

    Google Scholar 

  30. Thomas, T.: Russia’s reflexive control theory and the military. J. Slav. Mil. Stud. 17(2), 237–256 (2004). https://doi.org/10.1080/13518040490450529

    Article  Google Scholar 

  31. Waltzman, R.: SASC Testimony: The Weaponization of Information (2017)

    Google Scholar 

  32. Jaitner, M., Kantola, H.: Applying principles of reflexive control in information and cyber operations. J. Inf. Warf. 15(4), 27–38 Fall (2016)

    Google Scholar 

  33. Ruocco, A., Buchheit, N., Ragsdale, D.: A combined offensive/defensive network model. In: 1st Annual IEEE Systems, Man, and Cybernetics Information Assurance Workshop, West Point, NY, June 2000, pp. 14–18. Accessed 22 Aug 2019 [Online]

    Google Scholar 

  34. Dobson, T.K.: Entropy and self-organization—an open system approach to the origins of homeland security threats. Thesis, Naval Postgraduate School, Monterey, California (2015)

    Google Scholar 

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Correspondence to John Bicknell .

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Bicknell, J., Krebs, W. (2021). Process Mining Organization Email Data and National Security Implications. In: Braha, D., et al. Unifying Themes in Complex Systems X. ICCS 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-67318-5_15

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