Identity theft

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

A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT.

Abstract: At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

Risk management approach on identity theft in biometric systems context

Abstract: This paper introduces issues of risk management applied to biometrics. A biometrics study has been recently carried out by the ICT unit team and biometric technologies have been examined from a SELT perspective (social, economic, legal and technological). Different threats have been highlighted, such as identity theft. This paper presents an introduction on the risk "identity theft" in the information society and describes the identity theft as a real threat of the wide implementation of biometrics. Based on the results of the biometrics study and aiming at illustrating risk management on biometrics, the last section deals with the application of a risk process model on the identity theft in biometric systems context.

Anticipating Mobile Phone ‘Smart Wallet’ Crime: Policing and Corporate Social Responsibility

Abstract: Policing continues to struggle with the wave of mobile phone theft that emerged from the mid-1990s onwards. In this decade, the rate of increase may be waning, but the next wave may be approaching. Mobile phone smart wallets combine smart card technology with mobile phones, and the potential for identity theft and financial crime---and hence the attractiveness of theft---is likely to increase with smart wallets. This could spur new forms of theft, violence and other crimes. However, the market testing of technologies in Japan may be inappropriate for crime-proofing purposes, because of Japan’s low crime rate. The criminogenic potential of smart card and mobile smart wallet technologies warrants further examination. If policing is to avoid a potential crime problem, discussions with manufacturers should begin before the problem takes hold.

The evolution of fraud: Ethical implications in the age of large-scale data breaches and widespread artificial intelligence solutions deployment

Abstract: Artificial intelligence is being rapidly deployed in all contexts of our lives, often in subtle yet behavior nudging ways. At the same time, the pace of development of new techniques and research advancements is only quickening as research and industry labs across the world leverage the emerging talent and interest of communities across the globe. With the inevitable digitization of our lives, increasingly sophisticated and ever larger data security breaches in the past few years, we are in an era where privacy and identity ownership are becoming a relic of the past. In this paper, we will explore how large-scale data breaches, coupled with sophisticated deep learning techniques, will create a new class of fraud mechanisms allowing perpetrators to deploy “Identity Theft 2.0”.

Identity Theft as a Teachable Moment

Abstract: This paper examines how instances of identity theft that are sufficiently severe to induce consumers to place an extended fraud alert in their credit reports affect their risk scores, delinquencies, and other credit bureau variables on impact and thereafter. We show that for many consumers these effects are relatively small and transitory. However, for a significant number of consumers, especially those with lower risk scores prior to the event, there are more persistent and generally positive effects on credit bureau variables, including risk scores. We argue that these positive changes for subprime consumers are consistent with the effect of increased salience of credit file information to the consumer at the time of the identity theft.