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Detecting botnet signals using process mining

Author

Listed:
  • John W. Bicknell

    (CEO More Cowbell Unlimited, Inc)

  • Werner G. Krebs

    (CEO Acculation, Inc)

Abstract

Detecting and elucidating botnets is an active area of research. Using explainable, highly scalable Apache Spark-based artificial intelligence, process mining technologies are presented which illuminate bot activity within terrorist Twitter data. A derived hidden Markov model suggests that bot logic uses information camouflage in order to disguise intentions similar to World War II Nazi propagandists and Soviet-era practitioners of information warfare enhanced with reflexive control. A future effort is presented which strings together best of breed techniques into a composite classification algorithm in order to improve continually the discovery of malicious accounts, understand cross-platform weaponized botnet dynamics, and model adversarial information warfare campaigns recursively.

Suggested Citation

  • John W. Bicknell & Werner G. Krebs, 2021. "Detecting botnet signals using process mining," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 161-178, June.
    • Handle: RePEc:spr:comaot:v:27:y:2021:i:2:d:10.1007_s10588-020-09320-x
    DOI: 10.1007/s10588-020-09320-x
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