journal article

Detecting botnet signals using process mining

Bicknell J.W.; Krebs W.G.

Publication year: 2021
Source / journal: computational and mathematical organization theory
Country of origin: USA
Language: English
Authors’ affiliations: CEO More Cowbell Unlimited, Inc, 501 4th Street, Unit 795, Lake Oswego, 97034, OR, United States, CEO Acculation, Inc, 5482 Wilshire Blvd., #342, Los Angeles, 90036, CA, United States
Import source: Scopus export

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. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Author keywords

cognitive security, information warfare, misinformation, process mining, reflexive control, social media

Open DOI