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.
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