An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques

Title

An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques

Creator

Abhirami K.; Pani A.K.; Manohar M.; Kumar P.

Description

This paper is primarily focused on E-commerce fraud detection using machine learning techniques. There are many different ways to detect E-commerce fraud using machine learning approach. In this work, comparison study is conducted between various available machine learning algorithms to detect the online frauds. During the comparative study, focus is underlined on comparison of all the algorithms to identify the fraud transactions. When compared to other algorithms, such as support vector machine, Decision Tree, K-nearest neighbour and Random Forest, it has been observed that Logistic regression gives better result among all machine learning algorithms. 2021 IEEE.

Source

Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021, pp. 826-831.

Date

2021-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Credit card; Decision Tree(DT); Fraud detection; Frauds; K-Nearest Neighbor; Logistic regression; Random Forest(RF); Supervised machine learning algorithm; Support Vector Machine(SVM)

Coverage

Abhirami K., School of Engineering and Technology Christ(Deemed To Be University), Department of Computer Science and Engineering, Bangalore, India; Pani A.K., School of Engineering and Technology Christ(Deemed To Be University), Department of Computer Science and Engineering, Bangalore, India; Manohar M., School of Engineering and Technology Christ(Deemed To Be University), Department of Computer Science and Engineering, Bangalore, India; Kumar P., Motihari College of Engineering, Department of Computer Science and Engineering, Bihar, Motihari, India

Rights

Restricted Access

Relation

ISBN: 978-166543368-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICOSEC51865.2021.9591720

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123181008&doi=10.1109%2fICOSEC51865.2021.9591720&partnerID=40&md5=482e6b5af6f82f2708ea42139331d2d4