Internet Banking Fraud Detection Using Deep Learning Based on Decision Tree and Multilayer Perceptron

被引：0

作者：

Kataria, Sonal ^{[1
]}

Nafis, Md Tabrez ^{[1
]}

机构：

[1] Jamia Hamdard, Dept Comp Sci & Engn, New Delhi, India

来源：

PROCEEDINGS OF THE 2019 6TH INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM) | 2019年

关键词：

fraud detection; online banking fraud detection; neural network; online banking fraud; deep learning; artificial neural network; decision tree; multilayer perceptron algorithm; three layer perceptron;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Fraud transactions have become a growing problem in the online banking sphere. As technology progresses, fraudsters also change their methods of committing fraud. There are also emerging technologies that allow fraudsters to mimic the transaction behavior of genuine customers and they also keep changing their methods so that it is difficult to detect fraud. This paper discusses the importance of fraud detection methods and compares Hidden Markov Model, Deep Learning, and Neural Network that are used to detect fraud in online banking transactions.

引用

页码：1298 / 1302

页数：5

共 50 条

[41] Deep Learning Based Breast Cancer Detection Using Decision Fusion
Manali, Dogu
Demirel, Hasan
Eleyan, Alaa
COMPUTERS, 2024, 13 (11)
[42] Deep learning-based credit card fraud detection in federated learning
Reddy, Vadisena Venkata Krishna
Reddy, Radha Vijaya Kumar
Munaga, Masthan Siva Krishna
Karnam, Balaji
Maddila, Suresh Kumar
Kolli, Chandra Sekhar
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 255
[43] Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features
Sridharan, Naveen Venkatesh
Sugumaran, Vaithiyanathan
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2021,
[44] Efficient detection of phishing websites using multilayer perceptron
Odeh A.
Keshta I.
Abdelfattah E.
International Journal of Interactive Mobile Technologies, 2020, 14 (11) : 22 - 31
[45] Hydrodynamic detection based on multilayer perceptron and optimization using dynamic mode decomposition
Du, Peng
Zhao, Sen
Xing, Chengming
Chen, Xiaopeng
Hu, Haibao
Ren, Feng
Zhang, Miao
Xie, Luo
Huang, Xiao
Wen, Jun
OCEAN ENGINEERING, 2023, 278
[46] Detecting Childhood Pneumonia Using Handcrafted and Deep Learning Cough Sound Features and Multilayer Perceptron
Sharan, Roneel V.
Qian, Kun
Yamamoto, Yoshiharu
2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC, 2023,
[47] Transmission Line Fault Detection Based on Multilayer Perceptron
Yang, Hengyu
2022 INTERNATIONAL CONFERENCE ON BIG DATA, INFORMATION AND COMPUTER NETWORK (BDICN 2022), 2022, : 778 - 781
[48] FBG Sensor Signal Detection Technique Using Multilayer Perceptron Approach for Internet of Things (IoT) Application
Dehnaw, A. M.
Manie, Y. C.
Chen, Y. Y.
Li, J. W.
Peng, P. C.
2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TAIWAN), 2020,
[49] ANFIS learning using expectation maximization based Gaussian mixture model and multilayer perceptron learning
Jabeen, Sadaf
Baig, Mubasher
Awais, Mian Muhammad
APPLIED SOFT COMPUTING, 2023, 149
[50] Knowledge-based Financial Statement Fraud Detection System: Based on an Ontology and a Decision Tree
Tang, Xiao-Bo
Liu, Guang-Chao
Yang, Jing
Wei, Wei
KNOWLEDGE ORGANIZATION, 2018, 45 (03): : 205 - 219

← 1 2 3 4 5 →