Identity theft

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

Cyber Identity Theft

Abstract: Information and communication technologies (ICTs) provide substantial benefits to governments, organizations and individuals through providing low cost, instantaneous, global communication capabilities. However, an unintended consequence of these new technologies is their use for criminal purposes. The technology can be used as the mechanism for organizing and committing criminal activity and as a means of protecting criminals against detection and punishment. Cyber identity theft is an internationally recognized problem resulting from the introduction of new information technologies. This chapter provides an overview of cyber identity theft and related fraud, describing the impact of cyber identity theft on governments, organizations, law enforcement agencies and individuals. Methods currently being used, or proposed, to combat cyber identity fraud are outlined and the potential impact of these examined. The tension between using technological solutions to reduce cyber identity theft and privacy and civil liberties concerns is explored.

Method and system for preventing identity theft and providing credit independent completion of transactions

Abstract: A method and system for preventing identity theft and providing an anonymous payment mechanism, such as a payment card, and for completing transactions using the mechanism. The payment mechanism is prepaid and has associated therewith an address, a unique identifier for the mechanism (e.g., card number), and a password. Transactions are completed by verifying the mechanism identifier, the provided address, and the provided password, and by verifying that a balance sufficient for the transaction remains in the account for the mechanism. Remote payment, such as via the Internet, may be accomplished by requiring delivery to the provided address. Variations allow replenishment of the account balance, cancellation of the card, including automatic cancellation after a predetermined time period, and for adding additional features, such as biometric identification.

The Fair and Accurate Credit Transactions Act of 2003

Abstract: The Fair and Accurate Credit Transactions Act of 2003 (the “FACT Act”) made a number of amendments to the Fair Credit Reporting Act of 1996 intended to protect consumers against identity theft, increase the accuracy of consumer reports, and grant consumers greater control over marketing solicitations. Many provisions of the FACT Act will impact the structured finance industry. By its terms, the FACT Act applies to nearly every business that regularly extends credit to consumers. Further, the Act imposes many requirements on entities that use consumer credit information or report information to national consumer reporting agencies. Securitizers and other secondary market participants should not only develop FACT Act compliance procedures to the extent that they use or report credit information, but should also ensure that the lenders with which they do business meet their obligations under the Act.

CyberProtector: Identifying Compromised URLs in Electronic Mails with Bayesian Classification

Abstract: Embedding malicious URLs in e-mails is one of the most common web threats facing the internet community today. Malicious URLs have been widely used to mount various cyber-attacks like spear phishing, pharming, phishing and malware. By falsely claiming to be a trustworthy entity, users are lured into clicking on these compromised links to divulge vital information such as usernames, passwords, or credit card details and unknowingly succumb to identity theft. Hence, the detection of malicious URLs in e-mails is very essential so as to help internet users implement safe practices and as well prevent them from becoming victims of fraud. This paper explores how malicious links in e-mails can be detected from the lexical and host-based features of their URLs to protect users from identity theft attacks. This research uses Naive Bayesian classifier as a probabilistic model to detect if a URL is malicious or legitimate. The Naive Bayesian classifier is used to count up the occurrence of each feature in an e-mail and calculate the cumulative score. If the cumulative score is greater than the given threshold, the URL is considered malicious otherwise the URL is legitimate.