Identity theft

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

Detection and Response of Identity Theft within a Company Utilizing Location Information

Abstract: Accidents of information leakage and system hacking caused by identity theft in the organization/company have happened frequently. Many companies make a lot of efforts to prevent account theft such as access control, multi-factor authentication, encryption, and response to malicious code. It is not easy to apply them to the company because of less efficiency and inconvenience. In this paper, countermeasure against the threat of hacking is presented with the methods which is low-cost and with low false detection. The methods do not infringe privacy and decrease the efficiency of work through detection of identity theft by utilizing the physical information of employees.

Cyber Reconnaissance, Surveillance and Defense

Abstract: At a time when online surveillance and cybercrime techniques are widespread, and are being used by governments, corporations, and individuals, Cyber Reconnaissance, Surveillance and Defense gives you a practical resource that explains how these activities are being carried out and shows how to defend against them. Expert author Rob Shimonski shows you how to carry out advanced IT surveillance and reconnaissance, describes when and how these techniques are used, and provides a full legal background for each threat. To help you understand how to defend against these attacks, this book describes many new and leading-edge surveillance, information-gathering, and personal exploitation threats taking place today, including Web cam breaches, home privacy systems, physical and logical tracking, phone tracking, picture metadata, physical device tracking and geo-location, social media security, identity theft, social engineering, sniffing, and more. Understand how IT surveillance and reconnaissance techniques are being used to track and monitor activities of individuals and organizations Find out about the legal basis of these attacks and threats - what is legal and what is not - and how to defend against any type of surveillance Learn how to thwart monitoring and surveillance threats with practical tools and techniques Real-world examples teach using key concepts from cases in the news around the world

Securing or Protecting from Theft, Social Security or Other Sensitive Numbers in a Computerized Environment

Abstract: Use of a database/website or similar system, to store identification or other sensitive numbers, together with email addresses or other contact data in a linked association, for remote access by an organization to initiate usage notification to the true owner of the number collected, and to check a Fraud Alert status or similar setting for the number, and to compare the email address or other contact data obtained from a customer, not to include a PIN, to data stored in the database with the number collected, all of which, depending on the data components entered, will achieve deterrence of identity theft, rapid notification of number usage, rapid communication of a fraud alert or similar status, success or failure in obtaining or establishing an acceptable level of certainty that the customer is the true owner of the number, and a locking/unlocking capability for the number owners within the system domain.

Email phishing detection and prevention by using data mining techniques

Abstract: Social engineering has emerged as a serious threat in virtual communities and is an important means to attack information systems. The services used by today's knowledge workers prepare the base for complicated social engineering attacks. Phishing is a kind of technically generated social engineering attack and is the type of identity theft that uses the social engineering techniques and complex attack vectors to harvest financial information from unsuspecting consumers. It is a kind of attack in which phishers use spoofed emails and fraudulent web sites to trick people into giving personal information. Victims perceive these emails as trusted, while in reality they are the work of phishers interested in identity theft. Therefore, there is an urgent need for anti-phishing solutions and hereabout have been identified, a number of solutions to mitigate phishing attacks have been suggested. In this work, a phishing detection method is proposed by using machine learning and data mining techniques. Success rate of %89 has been achieved against phishing attacks coming from email messages.