Open AccessIdentity Theft Detection Using Machine Learning
Author(s) -
Vanshita Agarwal
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37696
Subject(s) - identity theft , cybercrime , harm , password , identity (music) , computer security , internet privacy , computer science , identification (biology) , phishing , artificial intelligence , world wide web , the internet , political science , law , acoustics , biology , physics , botany
Online Identity Theft is known to be quite possibly the most genuine and developing dangers to people and organizations for the past decade because of the colossal financial harm caused by it. It is a critical form of cybercrime which uses information such a victim’s name, bank details, email address, passwords, passport or identification details, and other valuable information to gain access to accounts. This aim of this research is to make use of Machine Learning algorithms for the detection of such crimes. Keywords: identity theft; cybercrime; Machine Learning; detection; algorithms