Detecting botnet signals using process mining

John W. Bicknell () and Werner G. Krebs ()
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John W. Bicknell: CEO More Cowbell Unlimited, Inc
Werner G. Krebs: CEO Acculation, Inc

Computational and Mathematical Organization Theory, 2021, vol. 27, issue 2, No 3, 178 pages

Abstract: 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.

Keywords: Process mining; Information warfare; Cognitive security; Social media; Misinformation; Reflexive control (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10588-020-09320-x

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