Content based fraudulent website detection using supervised machine learning techniques
Fraudulent websites pose as legitimate sources of information, goods, product and services are propagating and resulted in loss of billions of dollars. Due to several undesirable impacts of Internet fraud and scam, several studies and approaches are focused to identify fraudulent Internet websites,...
Saved in:
Main Authors: | Maktabar, Mahdi, Zainal, Anazida, Maarof, Mohd. Aizaini, Kassim, Mohamad Nizam |
---|---|
Format: | Conference or Workshop Item |
Published: |
2018
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/81884/ http://dx.doi.org/10.1007/978-3-319-76351-4_30 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Fraudulent e-Commerce website detection model using HTML, text and image features
by: Khoo, Eric, et al.
Published: (2020) -
Fraudulent detection model using machine learning techniques for unstructured supplementary service data
by: Olugbenga, Akinje Ayorinde
Published: (2021) -
Fraudulent detection model using machine learning techniques for unstructured supplementary service data
by: Akinje, Ayorinde O., et al.
Published: (2021) -
Prototype regularized manifold regularization technique for semi-supervised online extreme learning machine
by: Muhammad Zaly Shah, Muhammad Zafran, et al.
Published: (2022) -
An adaptive intrusion detection model for dynamic network traffic patterns using machine learning techniques
by: Zainal, Anazida
Published: (2011)