Detection of Fraudulent Behavior Using the Combined Algebraic and Machine Learning Approach

被引：0
|
作者：
Letychevskyi, Oleksandr [1 ]
Polhul, Tetiana [2 ]
机构：
[1] Glushkov Inst Cybernet NASU, Dept Digital Automata Theory, Kiev, Ukraine
[2] Vinnytsia Natl Tech Univ, Comp Sci Dept, Vinnytsia, Ukraine
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2019年
关键词：
machine learning; behavior algebra; blockchain; fuzzy logic; predicate transformer; symbolic modeling;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents two approaches to detecting fraudulent behavior: the machine learning technique, which was developed to recognize fraud in the Internet purchasing system; the algebraic approach, which was developed for the detection of attacks in blockchain-based systems. The machine learning approach uses technique of translating heterogeneous data to homogenous data and builds the classification model based on fuzzy logic rules for fraud recognition. The algebraic approach uses behavior algebra methods and symbolic modeling techniques.
引用
收藏
页码：4289 / 4293
页数：5
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