Implementing Account Takeover and Phishing Detection Models to Mitigate E-Commerce Fraud

The rise of e-commerce has rapidly changed the operating way of businesses, but simultaneously, it has opened up numerous avenues for fraud, it has been noticed through account takeovers and phishing attacks. These means of cybercrime can thus lead to a significant financial loss and reputation damage to online retailers. With the rising growth of online platforms comes the sophistication of fraudsters. One of the objectives behind this research is developing and analyzing models that can easily discover account takeovers and real-time phishing attempts on the systems to establish whether it is feasible to apply the high-end detection models integrated with machine learning techniques for dimming e-commerce frauds. The research consists of the following structure such as it features a literature review that provides a background overview of the problem then it consists of methods of how models are designed and evaluated. It also provides results on performance followed by discussions on effectiveness. It provides an overall outcome of the research to pinpoint key findings and directions for future research.

Authors

Institutions

Publication Details

Journal
Kuwait Journal of Machine Learning
Published
2024-12-24
DOI
https://doi.org/10.52783/kjml.255
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Implementing Account Takeover and Phishing Detection Models to Mitigate E-Commerce Fraud

Bhageerath Bogi
Kuwait Journal of Machine Learning
Spam and Phishing Detection
article

Implementing Account Takeover and Phishing Detection Models to Mitigate E-Commerce Fraud

Bhageerath Bogi
article en

Abstract

The rise of e-commerce has rapidly changed the operating way of businesses, but simultaneously, it has opened up numerous avenues for fraud, it has been noticed through account takeovers and phishing attacks. These means of cybercrime can thus lead to a significant financial loss and reputation damage to online retailers. With the rising growth of online platforms comes the sophistication of fraudsters. One of the objectives behind this research is developing and analyzing models that can easily discover account takeovers and real-time phishing attempts on the systems to establish whether it is feasible to apply the high-end detection models integrated with machine learning techniques for dimming e-commerce frauds. The research consists of the following structure such as it features a literature review that provides a background overview of the problem then it consists of methods of how models are designed and evaluated. It also provides results on performance followed by discussions on effectiveness. It provides an overall outcome of the research to pinpoint key findings and directions for future research.

Kuwait Journal of Machine LearningVol. 3(2)
Film Independent (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 62%
Spam and Phishing Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Implementing Account Takeover and Phishing Detection Models to Mitigate E-Commerce Fraud — Bhageerath Bogi · Kuwait Journal of Machine Learning (2024) | TGRS Research Map | TGRS