Identity theft

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

Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks

Abstract: In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False Positive Rate) below $1\%$. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns.

Trust and Compliance Effects of Taxpayer Identity Theft: A Moderated Mediation Analysis

Abstract: We experimentally investigate how tax authority responsibility for preventing identity theft and tax authority responsiveness following identity theft influence taxpayers' trust in the tax...

Determination of performance to verify the synthetic identity theft by training the neural networks

Abstract: This paper presents a method to analyse the various identities of a user and thus determine if any synthetic identity theft has been committed. Here three type of data is taken i.e., Input dataset (X), Normal dataset (Y) and Target Dataset (Z) are taken. The various identities used may be text or string data such as Candidate's Name, Date of Birth, Time of Birth Place of Birth, Home Address, Father's Name, Mother's Name, Husband's/ Wife's Name, Ration Card, Aadhar card Number, Voter's ID, Pan Card Number, SSLC Marks Card, Degree Proof, Blood Group, Face Image, Iris Image, Physical Features(Extra Thumb), Mole Marks, Injury Marks, Specimen Signature, Telephone Number, Mobile Number, Passport Number and Driving License Number. The various identities are classified in the category as 100% — High Identity with a correct information, 75% — Medium Identity with partial correct information and 30% — Low Identity with a wrong information. Each user are given the various score. The input values ranges from 0% to 100% for the various identity that is available. The normal values also ranges from 0% to 100%. The expected values are either 0% or 100%. With the above values training is given to the neural networks and the progress is obtained for the epoch values, time, performance, gradient and validation checks. The Performance, training state, confusion matrix and receiver operating characteristics are plotted for the plot interval of 9 epochs.

Facilitating online privacy on eCommerce websites: an Australian experience

Abstract: As traditional organizations using their websites for eCommerce transactions are increasing at an exponential rate, privacy concerns of users are also on the rise. To gain an insight into these concerns, existing policies and legislation, we conducted the research reported in this paper, in 2003. To augment the literature synthesis, a multiple case study analysis was conducted, based on six large organisations in Australia. Our research findings suggested that in the Australian context, an online privacy policy (OPP) on the website which complies with the Privacy Act, supported by few best practices are reasonably able to address online privacy concerns. However, these findings are restricted in time frame, indicative and relevant in the Australian context. Nevertheless, we hope to stimulate academic research enquiry and discussion forums through this research.

Cybercrime, Cybersecurity, and Financial Institutions Worldwide

Abstract: While the benefits of the Internet and other forms of computer networks are streamlining financial institutions, the same institutions are often among the first institutions to be affected by Cybercrime and Cybersecurity issues due to the financial incentives as well as their strategic place in each nation’s infrastructure and economy. We must look not only at the efficiency, but also at the negative aspects of the use of technology by financial institutions. Consumers as well as businesses must be well informed about conducting transactions in the safest manner possible. The nature of the Internet is cross-border, and thus Cybercrime and Internet Security issues involving financial institutions should be made known by international organizations, regional organizations, and when there have been cross-border law enforcement collaborations in investigations, extraditions, and so forth. At present, due to the fact that law is generally written at the national (or even state level, as is the case of Identity Theft law in the U.S.), there is a need for reporting of cross-border cases in the literature if such data can be obtained from law enforcement officials by scholars.