Showing papers on "Identity theft published in 2022"

Cybersecurity Practices for Social Media Users: A Systematic Literature Review

Abstract: In this paper, we present secondary research on recommended cybersecurity practices for social media users from the user’s point of view. Through following a structured methodological approach of the systematic literature review presented, aspects related to cyber threats, cyber awareness, and cyber behavior in internet and social media use are considered in the study. The study presented finds that there are many cyber threats existing within the social media platform, such as loss of productivity, cyber bullying, cyber stalking, identity theft, social information overload, inconsistent personal branding, personal reputation damage, data breach, malicious software, service interruptions, hacks, and unauthorized access to social media accounts. Among other findings, the study also reveals that demographic factors, for example age, gender, and education level, may not necessarily be influential factors affecting the cyber awareness of the internet users.

Digital body, identity and privacy in social virtual reality: A systematic review

Abstract: Social Virtual Reality (social VR or SVR) provides digital spaces for diverse human activities, social interactions, and embodied face-to-face encounters. While our digital bodies in SVR can in general be of almost any conceivable appearance, individualized or even personalized avatars bearing users’ likeness recently became an interesting research topic. Such digital bodies show a great potential to enhance the authenticity of social VR citizens and increase the trustworthiness of interpersonal interaction. However, using such digital bodies might expose users to privacy and identity issues such as identity theft: For instance, how do we know whether the avatars we encounter in the virtual world are who they claim to be? Safeguarding users’ identities and privacy, and preventing harm from identity infringement, are crucial to the future of social VR. This article provides a systematic review on the protection of users’ identity and privacy in social VR, with a specific focus on digital bodies. Based on 814 sources, we identified and analyzed 49 papers that either: 1) discuss or raise concerns about the addressed issues, 2) provide technologies and potential solutions for protecting digital bodies, or 3) examine the relationship between the digital bodies and users of social VR citizens. We notice a severe lack of research and attention on the addressed topic and identify several research gaps that need to be filled. While some legal and ethical concerns about the potential identity issues of the digital bodies have been raised, and despite some progress in specific areas such as user authentication has been made, little research has proposed practical solutions. Finally, we suggest potential future research directions for digital body protection and include relevant research that might provide insights. We hope this work could provide a good overview of the existing discussion, potential solutions, and future directions for researchers with similar concerns. We also wish to draw attention to identity and privacy issues in social VR and call for interdisciplinary collaboration.

A comprehensive survey on techniques to handle face identity threats: challenges and opportunities

Abstract: The human face is considered the prime entity in recognizing a person’s identity in our society. Henceforth, the importance of face recognition systems is growing higher for many applications. Facial recognition systems are in huge demand, next to fingerprint-based systems. Face-biometric has a highly dominant role in various applications such as border surveillance, forensic investigations, crime detection, access management systems, information security, and many more. Facial recognition systems deliver highly meticulous results in every of these application domains. However, the face identity threats are evenly growing at the same rate and posing severe concerns on the use of face-biometrics. This paper significantly explores all types of face recognition techniques, their accountable challenges, and threats to face-biometric-based identity recognition. This survey paper proposes a novel taxonomy to represent potential face identity threats. These threats are described, considering their impact on the facial recognition system. State-of-the-art approaches available in the literature are discussed here to mitigate the impact of the identified threats. This paper provides a comparative analysis of countermeasure techniques focusing on their performance on different face datasets for each identified threat. This paper also highlights the characteristics of the benchmark face datasets representing unconstrained scenarios. In addition, we also discuss research gaps and future opportunities to tackle the facial identity threats for the information of researchers and readers.

A Systematic Literature Review of Routine Activity Theory’s Applicability in Cybercrimes

Abstract: Cybercrimes are increasing at an alarming rate and cause detrimental effects to the victims. Routine Activity Theory (RAT) is commonly used to understand the factors influencing cybercrime victimization. However, there have been inconsistent findings on the applicability of RAT theory. This study performs a Systematic Literature Review analysis to consolidate and provide a coherent analysis of the related studies employing RAT theory for cybercrime victimization. The articles were also differentiated based on the cybercrimes topologies being investigated; (a) cybercrime dependent (hacking and malware) and (b) cybercrime enabled (phishing, fraud and identity theft). The findings suggest that a refined specification and operationalization of RAT’S construct tailoring to the types of cybercrimes can arguably yield more accurate application and interpretation of RAT Theory in cybercrimes. Consequently, this will address the inaccurate measurement issues of some of the RATS’s constructs, leading to inconclusive effects on cybercrime victimization. In addition, there is a need for more longitudinal studies to disentangle the effect of RAT’s construct during pre and post cybercrimes. Security advocates can apply the findings of this research to formulate relevant cybercrime awareness programs. The findings also shed some insights into which groups should be targeted for different cybercrime educational and awareness programs. This study can increase the awareness among citizens in terms of their online activities, their attributes and the types of protection from becoming cybercrime victims.

Mounting Cases of Cyber-Attacks and Digital Payment

Abstract: Digital transformation in financial transactions has changed the method of payment. We have witnessed a many-fold and rapid increase in the digital payment. As more individuals opt for digital payments, the potential of being exposed to cyber-attacks such as online fraud, theft of identity, and spyware or virus attacks is rising. Transaction on digital mode has led to an increase in internet-based crimes known by the term ‘cybercrime'. Cybercrime is an illegal act practiced by hackers on web applications, web browsers, and websites. Secured payment is critical for any company that deals with electronic payments and transactions. One of the most vital issues confronting players in the digital payment ecosystem is cyber security. The growth of such cyber-attacks can be attributed to various reasons, including a lack of knowledge and a poor digital payment infrastructure. To safeguard against threats of cybercrime, there are various cyber security techniques. This chapter deals in understanding the causes, threats, and solutions to cyber-attacks in digital payment methods.

On-Farm Data Security: Practical Recommendations for Securing Farm Data

Abstract: The growth in the use of Information and Communications Technology (ICT) and Artificial intelligence (AI) has improved the productivity and efficiency of modern agriculture, which is commonly referred to as precision farming. Precision farming solutions are dependent on collecting a large amount of data from farms. Despite the many advantages of precision farming, security threats are a major challenge that is continuously on the rise and can harm various stakeholders in the agricultural system. These security issues may result in security breaches that could lead to unauthorized access to farmers' confidential data, identity theft, reputation loss, financial loss, or disruption to the food supply chain. Security breaches can occur because of an intentional or unintentional actions or incidents. Research suggests that humans play a key role in causing security breaches due to errors or system vulnerabilities. Farming is no different from other sectors. There is a growing need to protect data and IT assets on farms by raising awareness, promoting security best practices and standards, and embedding security practices into the systems. This paper provides recommendations for farmers on how they can mitigate potential security threats in precision farming. These recommendations are categorized into human-centric solutions, technology-based solutions, and physical aspect solutions. The paper also provides recommendations for Agriculture Technology Providers (ATPs) on best practices that can mitigate security risks.

Are we Cyber aware? A cross sectional study on the prevailing Cyber practices among adults from Thiruvalla, Kerala.

Abstract: Background: Cyber-crime is described as any unlawful activity which is committed using any computing devices, like computer/smartphone and which is a part of internet. There are different methods by which cyber-crime is committed which include 1) attacks on computer systems, 2) cyber-bullying, 3) email spam, 4) phishing, 5) identity theft. Breaches in cyber security have become a severe danger to world security and the economy, compromising essential infrastructure and wreaking havoc on company performance, resulting in significant cognitive property loss. It is a sad reality that cybercrime cases have witnessed a steady spike. India has witnessed a significant increase in cases of cyber fraud and various cyber-related incidents in the last three years. The present study was done to assess the prevailing cyber practices among adults from Thiruvalla, Kerala.Methods: The present cross-sectional study was conducted among 340 adults in Thiruvalla, Kerala from January to June, and 2022. A semi-structured questionnaire was used to elicit information from the study participants after obtaining consent. The quantitative data collected was analysed using the software statistical package for social sciences. The results have been presented as tables and charts showing frequencies and percentages.Results: 133 out of the 350 study participants (38%) believed that it was important to be aware of cyber security risks in general while 52% (182) of the study participants considered cyber security awareness to be the only solution to the existing online scams.Conclusions: Widespread awareness campaigns are necessary to improve the cyber awareness of the community and thereby improve their cyber practices.

KDD Workshop on Machine Learning in Finance

Abstract: The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There is also the newly emerging threat of fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all these problems including semi-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions. We plan to invite regular papers, positional papers and extended abstracts of work in progress. We will also encourage short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers.

Survey on Fake Accounts Detection Algorithms on Online Social Networks

Abstract: AbstractOnline Social Networks (OSNs) are the most popular web services nowadays. They provide users with different kinds of services. Anyone can create his account on a certain OSN such as Facebook, using an email and password for registration. In addition, a single user can own one or more accounts. However, this feature has a lot of disadvantages and security drawbacks, such as creating fake accounts. A fake account is a profile that exists physically on OSN. Nonetheless, it is missing identity information such as names, last name, profile photo, and other profile attributes. Owners of fake accounts exploit them (accounts) for malicious internet activities like phishing, hacking, and more. Recently, this problem attracted considerably the research community. In this context, a lot of approaches have been emerged to solve fake account detection on OSNs. However, despite its importance, this field of research is still missing a systematic review. In this paper, we introduce a survey on sixteen kinds of research studies that have proposed solutions for solving the detection of fake accounts problem. We analyzed the following themes: Social networking platforms, evaluation metrics, machine learning algorithms, and models, data scale, features, and result accuracy were studied and analyzed in this survey.KeywordsFake accountAlgorithmsSocial media analysisSurveyMachine learning

An Awareness About Phishing Attack And Fake News Using Machine Learning Technique

Abstract: In this decade Social Media platforms and online websites plays an important role in bringing people together, gathering information and easy way for transferring the information. Social media news consumption provides both positive and negative impacts to this world. News is spreading rapidly using social media. On the other hand, it facilitates the spread of fake news. Wrong, and untruthful information. The phishing is one of the serious cyber threats to people's everyday lives and the internet environment in these attacks, the attacker impersonates a trusted entity with the intent of stealing sensitive information or the user's digital identity, such as account information, credit card and debit card numbers, CVV, pin and other user details. Therefore, Phishing website and fake news detection is an important need in our society. In this project phishing website detection is proposed using Logistic Regression and Naïve Bayes machine learning algorithms. Tf-idfVectorizer, CountVectorizer, Logistic Regression, Decision Tree Classifier and Random Forest Classifier are used for detecting the fake news.

Use of Machine Learning and Detect Fake Profiles in a Social Media Network

Abstract: The social network has become an important part of our lives today which is prone to online impersonation as well as fake accounts. These online profiles are used by intruders to cause harm in online social networks (OSNs) in order to perform malicious activities in form of privacy intrusion like theft identity. So it becomes necessary in OSN, to identify the account whether it is real or fake. In this article we have proposed several algorithms for classification like deep neural network and SVM (Support Vector Machine) algorithm. It also studies comparisons of these methods for classification based on the spam account dataset that is used to select the best.

Session hijacking vulnerabilities and prevention algorithms

Abstract: The concept of Internet security is studied by computer science as a safe medium for exchanging data while minimizing the likelihood of online threats. The extensive use of advanced web-based software in different industries such as education, retail, medical care, and payment systems, represents a security challenge for the programmers and an opportunity for the hackers to attack through session hijacking. Based on recent OWASP guidelines, this kind of attack is indeed one of the most frequent attacks that happens lately. Session hijacking happens as a result of poorly designed websites and a lack of security mechanisms, where the user's identity and session data are exposed. This paper will present this kind of vulnerability with the respective control mechanisms and will propose an approach for avoiding hijacking threats by using one-time cookies along with other prevention strategies. Keywords: session hijacking, vulnerability, one-time cookies.

Online Transaction Security Using Face Recognition: A Review

Abstract: Abstract: The growing development of the e-commerce market is of great significance in the world. In this online shopping process, the security of personal information and debit card or credit card information increases the popularity of e-commerce and is an important part. This paper provides limited information and is necessary for transferring money by online transactions by securing data and trust of customers. Facial recognition technology identifies a person's information through a digital image. It is automatically determined. It is mainly used in security systems. It matches facial recognition from different angles. It is mainly used in airports. It will recognize the face and we can avoid some unwanted fraud by using the facial recognition system. The fundamental gain of face popularity is used for fraud restrict and crime controlling motive due to the fact face pictures which have been archived and recorded, on the way to assist us to perceive someone later. Facial recognition identifies each distinct skin tone on the surface of a human face, such as curves on cheeks, eyes and nostrils, and more. The technology can also be used in very dark conditions and prevent identity theft.

Analysis of Supervised Machine Learning Algorithms in the Context of Fraud Detection

Abstract: In today’s era, where ‘time’ is considered as ‘money,’ people are completely depending on e-commerce and online banking for their routine purchases, shopping, and financial transactions. This increasing dependency on e-commerce are increasing fraud in online transactions, and credit card fraud is one example. Such malicious and unethical practices may cause identity theft and monitory loss to the people across the world. In this research paper, our effort is to identify the best Supervised Machine Learning algorithm that helps in classifying fraudulent and non-fraudulent transactions under credit card fraud on an imbalanced dataset. To conduct this research and compare the results, we have used five different Supervised Machine Learning Classification techniques. On implementing these machine learning techniques, it has been observed that both Supervised Vector Classifier and Logistic Regression Classifier perform better for detecting credit card fraud in an imbalanced dataset.

Exploring the determinants of victimization and fear of online identity theft: an empirical study

Abstract: The present study aims at understanding what factors contribute to the explanation of online identity theft (OIT) victimization and fear, using the Routine Activity Theory (RAT). Additionally, it tries to uncover the influence of factors such as sociodemographic variables, offline fear of crime, and computer perception skills. Data for the present study were collected from a self-reported online survey administered to a sample of university students and staff (N = 832, 66% female). Concerning the OIT victimization, binary logistic regression analysis showed that those who do not used credit card had lower odds of becoming an OIT victim, and those who reported visiting risky contents presented higher odds of becoming an OIT victim. Moreover, males were less likely than females of being an OIT victim. In turn, fear of OIT was explained by socioeconomic status (negatively associated), education (positively associated) and by fear of crime in general (positively associated). In addition, subjects who reported more online interaction with strangers were less fearful, and those reported more avoiding behaviors reported higher levels of fear of OIT. Finally, subjects with higher computer skills are less fearful. These results will be discussed in the line of routine activities approach and implications for online preventive behaviors will be outlined.

Reviewing Estimates of Cybercrime Victimisation and Cyber Risk Likelihood

Abstract: Across both the public and private sector, cyberse-curity decisions could be informed by estimates of the likelihood of different types of exploitation and the corresponding harms. Law enforcement should focus on investigating and disrupting those cybercrimes that are relatively more frequent, all else being equal. Similarly, firms should account for the likelihood of different forms of cyber incident when tailoring risk management policies. This paper reviews the quantitative evidence available for both cybercrime victimi-sation and cyber risk likelihood, providing a bridge between the academic fields of criminology and cybersecurity. We extract estimates from 48 studies conducted by a mix of academics, statistical institutes, and cybersecurity vendors using a range of data sources including victim surveys, case-control studies, and the insurance market. The victimisation estimates are categorised into: cyber attack; malware; ran-somware; fraudulent email; online banking fraud; online sales fraud; unauthorised access; Denial of Service; and identity theft. For each category, we display all estimates in the years 2017–2021. Our review shows: (i) firms face higher victimisation rates than individuals, which increases in the number of employees; (ii) global surveys reveal a consistent relative ranking of countries in ransomware victimisation; (iii) although trends could be identified within studies that collect longitudinal data, these trends tended to contradict each other when compared across studies; and (iv) broad categories with unclear consequences (e.g. malware and fraudulent emails) displayed higher variance and average values than categories associated with specific outcomes (e.g. identity theft or online banking fraud). We discuss the outlook for cybercrime and cyber risk research.

Knowledge Management Strategy for Handling Cyber Attacks in E-Commerce with Computer Security Incident Response Team (CSIRT)

Abstract: Electronic Commerce (E-Commerce) was created to help expand the market share network through the internet without the boundaries of space and time. However, behind all the benefits obtained, E-Commerce also raises the issue of consumer concerns about the responsibility for personal data that has been recorded and collected by E-Commerce companies. The personal data is in the form of consumer identity names, passwords, debit and credit card numbers, conversations in email, as well as information related to consumer requests. In Indonesia, cyber attacks have occurred several times against 3 major E-Commerce companies in Indonesia. In 2019, users’ personal data in the form of email addresses, telephone numbers, and residential addresses were sold on the deep web at Bukalapak and Tokopedia. Even though E-Commerce affected by the cyber attack already has a Computer Security Incident Response Team (CSIRT) by recruiting various security engineers, both defense and attack, this system still has a weakness, namely that the CSIRT operates in the aspect of handling and experimenting with defense, not yet on how to store data and prepare for forensics. CSIRT will do the same thing again, and so on. This is called an iterative procedure, one day the attack will come back and only be done with technical handling. Previous research has succeeded in revealing that organizations that have Knowledge Management (KM), the organization has succeeded in reducing costs up to four times from the original without using KM in the cyber security operations. The author provides a solution to create a knowledge management strategy for handling cyber incidents in CSIRT E-Commerce in Indonesia. This research resulted in 4 KM Processes and 2 KM Enablers which were then translated into concrete actions. The KM Processes are Knowledge Creation, Knowledge Storing, Knowledge Sharing, and Knowledge Utilizing. While the KM Enabler is Technology Infrastructure and People Competency.

Information about the identity of a probable criminal who commits illegal logging of forest plantations

Abstract: Следует отметить, что нелегальная добыча древесины растет, и с каж­дым годом увеличивается количество лиц, совершающих незаконную рубку лесных на­саж­дений. Лица, совершающие ее, — так называемые бригады черных лесорубов — ис­пользуют всё более изощренные способы совершения и сокрытия преступной деятель­ности, что требует развития и совершенствования кримина­листической методики рассле­дования незаконной рубки лесных насаждений. В представленной статье анализируются сведения о личности вероятного преступ­ника и мотивация его поведения при соверше­нии незаконной рубки лесных насаж­дений. Представлены точки зрения ряда авторов по данному вопросу, рассмотрены различные классификации категории личности прес­тупника, раскрыты наиболее важные. На основе данных, полученных при исследовании уголовных дел, автором статьи представлена классификация в зависимости от места сбыта (терри­тории) древесины: для дальнейшей реализации в пределах региона, в иные регионы и за пределы Российской Федерации. Обозначена роль каждого участника внутри прес­тупной группы в зависимости от выполняемых функций (лесорубов, заказчиков, лиц, осу­ществляющих транспортировку леса, организаторов). Раскрыты уровни изучения лич­ности вероятного преступника (обобщенный, групповой, индиви­дуальный). Представ­лена классификация личности вероятного преступника на лиц, совершающих незаконную рубку леса в бытовых и корыстных целях, а также раскрыты различия между ними. Предложена классификация лиц, совершивших незаконную рубку, в зависимости от про­ти­воправного деяния, на две группы: пер­вая — совершающие преступление с пос­ле­ду­ющей реализацией, вторая — для использования в личных целях. Рас­смот­рена роль дол­жностных лиц при совершении преступлений данной категории. Прово­дится анализ факторов, способствующих совершению преступлений, связанных с незакон­ной рубкой лестных насаждений. Приводятся характеристики преступников, соверша­ющих преступ­ления указанной категории, в зависимости от наличия судимости, сферы за­нятости, пола, возраста и др. Отмечается роль и значение знаний следователя (дозна­вателя) о содер­жании криминалистической характеристики сведений о личности вероят­ного преступ­ни­ка, совершившего незаконную рубку леса, для эффективного предупреж­дения преступ­ности в сфере лесных отношений. It should be noted that illegal timber extraction is growing and the number of persons committing illegal logging of forest plantations is increasing every year. The perpetrators are the so-called “brigades of black loggers” who use increasingly sophisticated methods of committing and concealing criminal activity, which requires the development and improvement of forensic methods of investigating illegal logging of forest plantations. The presented article analyzes information about the identity of a likely criminal and the motivation of his behavior when committing illegal logging of forest plantations. The author presents the authors’ points of view on this issue, considers various classifications of the criminal’s personality category, and reveals the most important ones. Based on the data obtained during the investigation of criminal cases, the author presented a classification depending on the place of sale (territory) of wood: for further sale within the region, to other regions and outside the Russian Federation. The role of each participant within the criminal group is indicated, depending on the functions performed (loggers, customers, persons carrying out the transportation of forests, organizers). The levels of studying the personality of a probable criminal (generalized, group, individual) are revealed. The classification of the personality of a probable criminal into persons committing illegal logging for domestic and mercenary purposes is presented, and the differences between them are revealed. The author proposes a classification of persons who have committed illegal logging, depending on the illegal act, into two groups: the first — committing a crime with subsequent implementation, the second — for use for personal purposes. The article examines the role of officials in the commission of crimes of this category. The analysis of the factors contributing to the commission of crimes related to illegal logging of flattering plantings is carried out. The characteristics of criminals who commit crimes of this category are given, depending on the presence of a criminal record, employment, gender, age, etc. The role and importance of the investigator’s (inquirer’s) knowledge about the content of the forensic characteristics of information about the identity of a likely criminal who committed illegal logging for effective crime prevention in the field of forest relations is noted.

Personal Identity Verification (PIV) of Federal Employees and Contractors

Abstract: Acknowledgements NIST would like to acknowledge the significant contributions of the Identity, Credential, and Access Management Subcommittee (ICAMSC) and the Smart Card Interagency Advisory Board (IAB) for providing valuable contributions to the development of technical frameworks on which this Standard is based. Special thanks to those who have participated in the business requirements meeting and provided valuable comments in shaping this Standard.

A Study on Brute Force Attack on T-Mobile Leading to SIM-Hijacking and Identity-Theft

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.

Identity theft fraud- major loophole for FinTech industry in India

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.

Practical identity recognition using WiFi's Channel State Information

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.

Fake Profile Detection Using Machine Learning Techniques

Abstract: Our lives are significantly impacted by social media platforms such as Facebook, Twitter, Instagram, LinkedIn, and others. People are actively participating in it the world over. However, it also has to deal with the issue of bogus profiles. False accounts are frequently created by humans, bots, or computers. They are used to disseminate rumors and engage in illicit activities like identity theft and phishing. So, in this project, the author’ll talk about a detection model that uses a variety of machine learning techniques to distinguish between fake and real Twitter profiles based on attributes like follower and friend counts, status updates, and more. The author used the dataset of Twitter profiles, separating real accounts into TFP and E13 and false accounts into INT, TWT, and FSF. Here, the author discusses LSTM, XG Boost, Random Forest, and Neural Networks. The key characteristics are chosen to assess a social media profile’s authenticity. Hyperparameters and the architecture are also covered. Finally, results are produced after training the models. The output is therefore 0 for genuine profiles and 1 for false profiles. When a phony profile is discovered, it can be disabled or destroyed so that cyber security problems can be prevented. Python and the necessary libraries, such as Sklearn, Numpy, and Pandas, are used for implementation. At the end of this study, the author will come to the conclusion that XG Boost is the best machine learning technique for finding fake profiles.

Anonymous Identity Authentication scheme for Internet of Vehicles based on moving target Defense

Abstract: As one of the effective methods to enhance traffic safety and improve traffic efficiency, the Internet of vehicles has attracted wide attention from all walks of life. V2X secure communication, as one of the research hotspots of the Internet of vehicles, also has many security and privacy problems. Attackers can use these vulnerabilities to obtain vehicle identity information and location information, and can also attack vehicles through camouflage.Therefore, the identity authentication process in vehicle network communication must be effectively protected. The anonymous identity authentication scheme based on moving target defense proposed in this paper not only ensures the authenticity and integrity of information sources, but also avoids the disclosure of vehicle identity information.

The Application of Role-Based Framework in Preventing Internal Identity Theft Related Crimes: A Qualitative Case Study of UK Retail Companies

Abstract: Abstract This paper aims to examine the challenges of preventing internal identity theft related crimes (IIDTRC) in the UK retail sector. Using an in-depth multiple case studies of a selected number of cross-functional management teams in the UK retail companies, management roles were analysed. We used semi-structured interview as a qualitative data collection technique and used Nvivo aided thematic analysis and interpretivism underpinned by Role-Based Framework (RBF) for analysis. Our findings revealed that vagueness of roles and lack of clarity in sharing data security responsibilities are the major challenges of preventing IIDTRC in UK retail companies. We suggest an application of RBF which provides a conceptual analysis for cross-functional management team to address the challenges of preventing IIDTRC. RBF enables clarity of shared roles where both information security and crimes prevention teams work in unison is required to prevent IIDTRC to maximise internal data security. Contributions for policymakers are offered in this paper.