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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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Journal ArticleDOI
TL;DR: In this article , a factor analysis was performed to determine the level of awareness and behavior patterns of social media users using the MANOVA method as data analysis and the TTAT model.
Abstract: Theft of credential data by entering a username and password on the login page until the account is open. The authentication process in applications and the web is used to identify ownership of user data, this is because there is a vulnerability to attacks in the use of unsafe usernames and passwords. There are several security issues, one of the most common being passwords. Most systems use passwords to verify user identity. However, these passwords come with major security issues as users tend to use ones that are easy to guess, use the same password across multiple accounts, write it down and store it on their devices. Hackers have many options using which to steal passwords or hack user accounts such as credential stuffing, phishing, password.spraying, bruce force, prior data breach / reused passwords, password reset, keystroke logging and local discovery. To overcome security attacks, Multi-Factor Authentication (MFA) techniques provide higher security guarantees. Public awareness to protect identity information is fundamental, identity theft can go through various channels and ways and this needs to be emphasized. Self-efficacy (security awareness), behavioral intention, avoidance motivation and avoidance behavior are factors that influence the object. This factor analysis aims to determine the level of awareness and behavior patterns of social media users. Factor analysis used the MANOVA method as data analysis and the TTAT model. Based on the results of the MANOVA tests of between – subjects effects, the factor of favorance motivation (r = 0.499 and Sig. 0.000) and behavioral intention (r = 0.427 and Sig. 0.000). There is a link between these factors, so users must avoid data theft through security measures using multi-factor authentication (MFA).
Journal ArticleDOI
TL;DR: In this article , the dangers waiting for social media users and modern methods of obtaining information were discussed, and the most important issues related to SOCMInt. techniques were also discussed.
Abstract: Social media plays an important role in almost everyone’s life. Users by publishing specific materials from their private lives provide valuable knowledge to online criminals. As a result, they are exposed to various forms of cybercrime, including identity theft, cyberbullying, and internet fraud. Therefore, social media must be monitored by the police and security services. Using SOCMInt. techniques. in the Internet space, it has a decisive influence on the overall national security. This article presents the dangers waiting for social media users and modern methods of obtaining information. The most important issues related to SOCMInt. techniques were also discussed.
Journal ArticleDOI
TL;DR: In this article , the authors used several machine learning techniques to discriminate between fake and authentic Twitter profiles based on characteristics such as follower and friend counts, status changes, and more. But, it also needs to address the problem of false profiles.
Abstract: Platforms for social media like Facebook, Twitter, Instagram, and others have a big impact on our lives. All across the world, people are actively engaged in it. But, it also needs to address the problem of false profiles. Fake accounts are regularly made by people, software, or machines. They are employed in the spread of rumors and illegal actions like phishing and identity theft. This project uses several machine learning techniques to discriminate between fake and authentic Twitter profiles based on characteristics such as follower and friend counts, status changes, and more. Twitter profile dataset, classifying genuine accounts as TFP and E13 and fake accounts as INT, TWT, and FSF. In this section, the author talks about neural networks, LSTM, XG Boost, and Random Forest. The important traits are picked to judge the veracity of a social media page. The architecture and hyperparameters are also discussed. Lastly, after the models have been trained, results are generated. As a result, the output is 0 for true profiles and 1 for fake profiles. It is possible to disable or delete a fake profile when it is found, preventing cyber security issues.
Journal ArticleDOI
TL;DR: In this article , a generic blockchain-IoT-based self-sovereign identity management framework called ChainDiscipline is proposed and implemented for healthcare and smart home data management based use cases.
Abstract: In today's complex Internet platform, online users need help to protect their online identity. Only sometimes, websites are very transparent about how user data will be collected, stored and processed by them. Sometimes Internet entities collect more online user information than required. These entities often share user identity-related data with third parties without consent. Existing traditional identity schemes need to be improved to stop and counter new ways of digital identity theft and fraud. Blockchain is a promising technology to strengthen the preservation of online users' digital identity due to its decentralised nature and robust data security features. In this paper, we proposed and implemented a generic blockchain- IoT-based self-sovereign identity management framework called ChainDiscipline. We have demonstrated the framework's oper- ability and functionality by implementing healthcare and smart home data management-based use cases.
Book ChapterDOI
01 Jan 2023
TL;DR: The use of PrivacyEnhancing Technologies in the field of data anonymization and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation as discussed by the authors , especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection.
Abstract: The use of Privacy-Enhancing Technologies in the field of data anonymisation and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation. Here, especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection. A workshop held at the IFIP Summer School on Privacy and Identity Management showed the complexity of this field and the need for further interdisciplinary research on the basis of an improved joint understanding of legal and technical concepts.

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