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Identity theft

About: Identity theft is a research topic. Over the lifetime, 2284 publications have been published within this topic receiving 31700 citations.


Papers
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Book
20 Aug 2016
TL;DR: The Basics of Cyber Safety: Computer and Mobile Device Safety Made Easy presents modern tactics on how to secure computer and mobile devices, including what behaviors are safe while surfing, searching, and interacting with others in the virtual world.
Abstract: The Basics of Cyber Safety: Computer and Mobile Device Safety Made Easy presents modern tactics on how to secure computer and mobile devices, including what behaviors are safe while surfing, searching, and interacting with others in the virtual world. The book's author, Professor John Sammons, who teaches information security at Marshall University, introduces readers to the basic concepts of protecting their computer, mobile devices, and data during a time that is described as the most connected in history. This timely resource provides useful information for readers who know very little about the basic principles of keeping the devices they are connected to?or themselves?secure while online. In addition, the text discusses, in a non-technical way, the cost of connectedness to your privacy, and what you can do to it, including how to avoid all kinds of viruses, malware, cybercrime, and identity theft. Final sections provide the latest information on safe computing in the workplace and at school, and give parents steps they can take to keep young kids and teens safe online.Provides the most straightforward and up-to-date guide to cyber safety for anyone who ventures online for work, school, or personal useIncludes real world examples that demonstrate how cyber criminals commit their crimes, and what users can do to keep their data safe

4 citations

Journal Article
TL;DR: The Phishing is an attempt by an individual or a group to thieve personal confidential information such as passwords, credit card information from unsuspecting victims for identity theft, financial gain and other fraudulent activities.
Abstract: Now-a-days online attacks have increased to a great extent and the most popular attack among them is phishing. Phishing can be basically defined as one kind of attack in which various attackers acquire the confidential and sensitive information of the victims. The Phishing is an attempt by an individual or a group to thieve personal confidential information such as passwords, credit card information from unsuspecting victims for identity theft, financial gain and other fraudulent activities. In phishing attack phishers attempt to fraudulently acquire sensitive information like users id, password, contact details, credit card information etc. by masquerading as a trustworthy person or business in an electronic communications. Thus, security in such cases should be very high to avoid the online attacks. So it is very much important for users to identify the fake website and avoid falling prey to it.

4 citations

Journal ArticleDOI
TL;DR: Patients whose identity has been stolen are vulnerable to both medical error and financial loss, and providers may suffer both reputation loss andfinancial loss.
Abstract: Medical identity theft is a crime with two victims: patients and providers. It is easy to commit and lucrative because healthcare record keeping and business interactions are complex and mainly electronic. Patients whose identity has been stolen are vulnerable to both medical error and financial loss. Providers may suffer both reputation loss and financial loss. There are steps to help prevent and to respond appropriately to medical identity theft.

4 citations

Journal ArticleDOI
01 Jan 2021
TL;DR: In this paper, the authors present the results of a usability study focused on three end-to-end encryption technologies for securing e-mail traffic, namely PGP, S/MIME, and Pretty Easy Privacy (pEp).
Abstract: This paper presents the results of a usability study focused on three end-to-end encryption technologies for securing e-mail traffic, namely PGP, S/MIME, and Pretty Easy Privacy (pEp). The findings of this study show that, despite of existing technology, users seldomly apply them for securing e-mail communication. Moreover, this study helps to explain why users hesitate to employ encryption technology in their e-mail communication. For this usability study, we have combined two methods: (1) an online survey, (2) and user testing with 12 participants who were enrolled in tasks requiring e-mail encryption. We found that more than 60% of our study participants (in both methods) are unaware of the existence of encryption technologies and thus never tried to use one. We observed that above all, users (1) are overwhelmed with the management of public keys and (2) struggle with the setup of encryption technology in their e-mail software. Nonetheless, 66% of the participants consider secure e-mail communication as important or very important. Particularly, we found an even stronger concern about identity theft among e-mail users, as 78% of our participants want to make sure that no other person is able to write e-mail on their behalf.

4 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: The main premise of the approach is a hybrid machine learning model comprising of two steps- checking with a blacklist and whitelist, and heuristics based detection, to increase the accuracy of the proposed algorithm.
Abstract: Phishing is a kind of a social engineering attack. The attacker poses as a legitimate entity and communicates with the victim through some mode of communication. The user is prompted to open a link which has been designed to look similar to a legitimate website, or is prompted to relay sensitive credentials over the phone. The attacker steals the users' information to perform identity theft, account hijacking, etc. In this paper, we focus on URL based phishing attacks. Most of the solutions that we investigated focused more on the classification algorithms rather than clustering. Our aim is to experiment and compare the results of both of these types of algorithms. The main premise of our approach is a hybrid machine learning model comprising of two steps- checking with a blacklist and whitelist, and heuristics based detection, to increase the accuracy of the proposed algorithm.

4 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202384
2022165
202178
2020107
2019108
2018112