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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.


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Proceedings ArticleDOI
22 May 2010
TL;DR: A security model for Lustre based on PKI can reduce safety loopholes and enhance security in Lustre file system, such as identity theft, data interception, data modification and replay-attack.
Abstract: Lustre file system can improve I/O throughput in the clusters effectively, but there still be some security problems in TCP/IP network environment, such as identity theft, data interception, data modification and replay-attack. Lustre is planning to use Kerberos security mechanism which can not solve some problems in enterprise-wide, such as overhead, digital signature and password attack. To the problems, this paper presents a security model for Lustre based on PKI. The model includes a certificate management module and a client access module. Certification management mechanism based on PKI is adopted in the certificate management module. Bidirectional identity authentication and digital signature are applied in the client access module. Random number must be checked during authentication. The security model can reduce safety loopholes and enhance security in Lustre file system, such as identity theft, data interception, data modification and replay-attack.

1 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a bandage-cover cryptographer (BCC), which is completely software-defined and protocol-free, and this new cryptographic approach can enable camouflages to confuse data-mining robots, which are often encountered in the cyber world nowadays.
Abstract: Cyber security has become an important problem nowadays as almost everyone is often linked to the Internet for business and entertainment. Conventional cryptographers fail to address timely issues regarding cyber-attacks, such as cyber identity theft. In this work, we propose a novel idea, namely, a bandage-cover cryptographer (BCC), which is completely software-defined and protocol-free. Besides, this new cryptographic approach can enable camouflages to confuse data-mining robots, which are often encountered in the cyber world nowadays. Because all of the existing cryptographers aim to protect the entire data (document and file) altogether, they cannot have camouflagibility to mislead data-mining robots. Conversely, by our proposed novel BCC, one can select arbitrary contexts or parts of the data (related to individual identify and/or private confidential information) under protection. To evaluate such a first-ever cryptographer capable of misleading data-mining robots, we define two new metrics, namely: 1) vulnerability and 2) camouflage rates. The theoretical analyses of vulnerability rate and camouflage rate for our proposed new BCC are also presented in this article to demonstrate the corresponding effectiveness.

1 citations

Proceedings ArticleDOI
06 Jun 2022
TL;DR: In this article , the authors analyze the T-Mobile data breach and how it opens the door to identity theft and many other forms of hacking such as SIM Hijacking, which is a form of hacking in which bad actors can take control of a victim's phone number allowing them to bypass additional safety measures currently in place to prevent fraud.
Abstract: The 2021 T-Mobile breach conducted by John Erin Binns resulted in the theft of 54 million customers' personal data. The attacker gained entry into T-Mobile's systems through an unprotected router and used brute force techniques to access the sensitive information stored on the internal servers. The data stolen included names, addresses, Social Security Numbers, birthdays, driver's license numbers, ID information, IMEIs, and IMSIs. We analyze the data breach and how it opens the door to identity theft and many other forms of hacking such as SIM Hijacking. SIM Hijacking is a form of hacking in which bad actors can take control of a victim's phone number allowing them means to bypass additional safety measures currently in place to prevent fraud. This paper thoroughly reviews the attack methodology, impact, and attempts to provide an understanding of important measures and possible defense solutions against future attacks. We also detail other social engineering attacks that can be incurred from releasing the leaked data.

1 citations

Journal ArticleDOI
TL;DR: In this paper , a conceptual model of the common fraud types in the FinTech industry is proposed to enhance the understanding of the key fraud-causing elements, and the authors suggest some preventive techniques to prevent corporate frauds.
Abstract: Purpose The fraud landscape for FinTech industry has increased over the past few years, certainly during the time of COVID-19, FinTech market reported rapid growth in the fraud cases (World Bank, 2020). Taking the consideration, the paper has qualitatively understood the loopholes of the FinTech industry and designed a conceptual model declaring “Identity Theft” as the major and the common fraud type in this industry. The paper is divided in two phases. The first phase discusses about the evolution of FinTech industry, the second phase discusses “Identity Theft” as the common fraud type in FinTech Industry and suggests solutions to prevent “Identity Theft” frauds. This study aims to serve as a guide for subsequent investigations into the FinTech sector and add to the body of knowledge regarding fraud detection and prevention. This study would also help organisations and regulators raise their professional standards in relation to the global fraud scene. Design/methodology/approach This paper revisits the literature to understand the evolution of FinTech Industry and the types of FinTech solutions. The authors argue that traditional models must be modernised to keep up with the current trends in the rapidly increasing number and severity of fraud incidents and however introduces the conceptual model of the common fraud type in FinTech Industry. The research also develops evidences based on theoretical underpinnings to enhance the comprehension of the key fraud-causing elements. Findings The authors have identified the most common fraud type in the FinTech Industry which is “Identity Theft” and supports the study with profusion of literature. “Identity theft” and various types of fraud continue to outbreak customers and industries similar in 2021, leaving several to wonder what could be the scenario in 2022 and coming years ahead (IBS Inteligence, 2022). “Identify theft” has been identified as one the common fraud schemes to defraud individuals as per the Association of Certified Fraud Examiners. There is a need for many of the FinTech organisations to create preventive measures to combat such fraud scheme. The authors suggest some preventive techniques to prevent corporate frauds in the FinTech industry. Research limitations/implications This study identifies the evolution of FinTech industry, major evidences of Identity Thefts and some preventive suggestions to combat identity theft frauds which requires practical approach in FinTech Industry. Further, this study is based out of qualitative data, the study can be modified with statistical data and can be measured with the quantitative results. Practical implications This study would also help organisations and regulators raise their professional standards in relation to the global fraud scene. Social implications This study will serve as a guide for subsequent investigations into the FinTech sector and add to the body of knowledge regarding fraud detection and prevention. Originality/value This study presents evidence for the most prevalent fraud scheme in the FinTech sector and proposes that it serve as a theoretical standard for all ensuing comparison.

1 citations

Proceedings ArticleDOI
14 Mar 2022
TL;DR: In this article , the authors proposed a nonintrusive identity recognition system based on analyzing WiFi's Channel State Information (CSI), which attenuated by a person's body and typical movements allows for a reliable identification.
Abstract: Identity recognition is increasingly used to control access to sensitive data, restricted areas in industrial, healthcare, and defense settings, as well as in consumer electronics. To this end, existing approaches are typically based on collecting and analyzing biometric data and imply severe privacy con-cerns. Particularly when cameras are involved, users might even reject or dismiss an identity recognition system. Furthermore, iris or fingerprint scanners, cameras, microphones, etc., imply installation and maintenance costs and require the user's active participation in the recognition procedure. This paper proposes a non-intrusive identity recognition system based on analyzing WiFi's Channel State Information (CSI). We show that CSI data attenuated by a person's body and typical movements allows for a reliable identification - even in a sitting posture. We further propose a lightweight deep learning algorithm trained using CSI data, which we implemented and evaluated on an embedded platform (i.e., a Raspberry Pi 4B). Our results obtained using real-world experiments suggest a high accuracy in recognizing people's identity, with a specificity of 98% and a sensitivity of 99%, while requiring a low training effort and negligible cost.

1 citations


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