Abstract
User and entity behavior analytics (UEBA) methods in fraud detection and advertising are widely used cognitive science methods in modern online banking systems. But profusion of marketing papers complicates true situation. Most of academic papers contain a systematic error: “Correct sample choice error”. Fed to the input the real data of user behavior in online banking do not nothing benefit. The paper will be submitted to the criticism of several methods on “mouse track analysis” and “keystroke dynamics” algorithms. New type of algorithms will be present: “preference-behavioral chain” methods. One “preference-behavioral chain” algorithm for social engineering detection will be presented.
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Slipenchuk, P., Epishkina, A. (2020). Practical User and Entity Behavior Analytics Methods for Fraud Detection Systems in Online Banking: A Survey. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_11
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DOI: https://doi.org/10.1007/978-3-030-25719-4_11
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