Mobile Biometrics Research Articles

Vulnerabilities in One Time Passwords and Protection Method Using Secret Key and Biometric Hash Code

In the present era, conducting financial transactions online is both simple and widely embraced for its convenience. However, engaging in internet-based financial transactions exposes individuals to potential threats like password attacks, malware, phishing, and other illicit activities. Recognizing these risks, many banks have enhanced security measures by incorporating One-Time Password (OTP) authentication alongside traditional login credentials. Despite these improvements, security issues persist in online transactions. To mitigate such concerns, OTPs can be delivered via SMS to the account owner's mobile number. Even with heightened security, internet banking remains susceptible to various attacks, including online phishing, Man-in-the-Middle attacks, SQL injection, and brute force attacks. To address these challenges, this proposed model introduces a Biometric mobile OTP-based transaction authentication method.

Mobile Biometric and Sentiment Data, Generative Artificial Intelligence and Behavior Tracking Tools, and Wearable Sensor-based and Connected Monitoring Devices in Immersive Interconnected 3D Worlds

Mobile Biometric and Sentiment Data, Generative Artificial Intelligence and Behavior Tracking Tools, and Wearable Sensor-based and Connected Monitoring Devices in Immersive Interconnected 3D Worlds

Immersive Remote Collaboration and Workplace Tracking Systems, Mobile Biometric and Sentiment Data, and Algorithmic Monitoring and Wearable Augmented Reality Technologies in Generative Artificial Intelligence-based Virtual Human Resource Management

Immersive Remote Collaboration and Workplace Tracking Systems, Mobile Biometric and Sentiment Data, and Algorithmic Monitoring and Wearable Augmented Reality Technologies in Generative Artificial Intelligence-based Virtual Human Resource Management

Deep Learning-Based Object Detection and Holographic Virtual Imaging Technologies, Mobile Biometric and Sentiment Data, and Digital Twin Simulation and Modeling Tools in the Metaverse Economy

Deep Learning-Based Object Detection and Holographic Virtual Imaging Technologies, Mobile Biometric and Sentiment Data, and Digital Twin Simulation and Modeling Tools in the Metaverse Economy

Metaverse Employee Socialization and Operations Management, Mobile Biometric and Sentiment Data, and Wearable Augmented Reality Devices in Immersive Workspaces

Metaverse Employee Socialization and Operations Management, Mobile Biometric and Sentiment Data, and Wearable Augmented Reality Devices in Immersive Workspaces

MoLaBSS: Server-Specific Add-On Biometric Security Layer Model to Enhance the Usage of Biometrics

With high-paced growth in biometrics, and its easy availability to capture various biometric features, it is emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. Organizations encourage their members to use biometrics, but they are hesitant to use them due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. We propose a server-specific add-on biometric security layer model (MoLaBSS) to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show good potential of this framework to enhance users’ confidence level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.

Cyber Forensic Challenges: Escaping Holes for Cyber Attackers Series 01: Extracting Forensic Evidences from Non- Functional Computer Devices

According to Forensic Terminology, Cyber Forensic Challenge is a state, a competitive situation, unavailability of reliable evidences and bottleneck constraint in Computer Forensic Investigation and its Procedures. A Forensic Challenge can either be Technical, Admistrative or Legislative/Legal and Anti-Forensic is an example of Technical Challenge, it adds complexity in acquiring and accessing an evidence or sets of evidences during Forensic Investigation. In real-world scenarios, it hurdles Forensic Examiners from acquiring an image of evidences to continue with Forensic Examinations and makes the Attackers or Adversaries "Scape Goats" during Criminal Prosecution or in a Corporate Forensic Investigation, as where it arises. In Public Computer Forensic domains, one of the Challenges is unavailability of Public Repositories of MACs, Bluetooth, IMEI and NFC Addresses -of Mobile Phones, WiFi Routers, Printers, Biometric Scanners and other IOT Devices(such as Amazon Alexa, Smart Watch and FitBit). Globally, not adequate, but there are reasonable numbers of such Repositories to overcome the challenges in the concerned fields and countable numbers of systems dedicated to such collections and innovative ones to name are CEIR(Central Equipment Identity Repository) for IMEI Databases(India), WIGLE for WiFi SSID(Global) but there is still shortfalls of ethical Repositories of Bluetooth Addresses (Private and Public) and Identifiers associated with NFC and RFID !

Mobile Biometrics and their Risks

The present article aims to introduce the ways of secure access control, with a special emphasis on biometric solutions on mobile devices. Apart from secure biometric data storage, which is also a very important aspect of this topic, there are several other types of threats. On the following pages we provide a short description of the possible risks of biometric systems. In order to understand the current status and attitude towards biometrics, we introduce our own survey as well.

Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Biometrics, as a form of authentication, has existed for several decades and shows no signs of slowing down. Extensive research has been carried out into enhancing systems either by improving error rates or ease of adoption by examining barriers to use. In this paper, we investigate factors of a biometric system that is likely to affect performance, in particular, focusing on mobile device implementation. By surveying the area, we have identified seven core factors that help to form a clearer understanding of what changes the performance of a system. These seven factors are Users, Modality, Environments, Diversity of Scenarios, System Constraints, Hardware and Algorithms and form `The Core Factors Affecting Mobile Biometric Performance'. We utilise these factors to illustrate the practicalities of mobile implementations and indicate future considerations to explore future performance enhancements and provide an informative overview to developers, implementers and testers of biometrics systems, enabling the binning of performance alterations within one of these factors.

Iris Anti-Spoofing Solution for Mobile Biometric Applications

The ability to provide reliable protection against counterfeiting is one of the key requirements for a biometric security system. Iris recognition as the technology emerging on mobile market is assumed to handle various types of spoof attacks to prevent compromise of the user’s personal data. A method of iris anti-spoofing is proposed in this work. It is based on applying of convolutional neural network and capable to work in real-time on the mobile device with highly limited computational resources. Classification of iris sample for spoof and live is made by a single frame using a pair of images: eye region and normalized iris. The following types of iris spoof samples are considered in this particular work: printed on paper, printed on paper with imposition of a contact lens, printed on paper with application of transparent glue. Testing of the method is performed on the dataset manually collected and containing all the mentioned spoof sample types. The method revealed its high performance in both classification accuracy and processing speed as well as robustness under uncontrollably changing environmental conditions, which are specific and significant when interacting with the mobile device.

A continuous combination of security & forensics for mobile devices

In this research, we introduce the concept of adversary identification in combination with continuous authentication. To protect the system from session hijacking, it is important to not only use the traditional access control at the beginning of a session but also continuously monitor the entire session whether the present user is still the legitimate user or not. In case an impostor is detected, the system should lock to avoid loss or disclosure of personal or confidential information. In many cases, it will be important to not only secure the system but also establish the identity of the impostor which could be seen as a deterrence measure or could be used as a shred of evidence in the court. This concept has not been introduced in this manner before, and it combines security and forensics continuously.We have performed a closed-set and an open-set experiment to validate our proof of concept with two different publicly available mobile biometrics datasets. Depending upon the dataset and used settings, we show that the adversary can be correctly identified with 82.2% to 97.9% of the attack cases for closed-set experiment and for the open-set experiment this performance ranges from 73.8% to 77.6%.

Accelerating mobile biometrics adoption: doing nothing is not an option

Smartphone users are becoming increasingly familiar with, and accepting of, biometrics to unlock their devices or log into apps. And many consumers expect – even sometimes demand – alternatives to PINs, passwords and tokens as a means to access devices and services. Yet despite these trends, many people still hesitate to sign up for mobile biometrics when offered and a range of commercial enterprises, especially banking and financial services firms, are still sceptical about using mobile biometrics to protect high-value transactions.

Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation

Mobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural candidate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular attention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies.

The overview of trends and challenges in mobile biometrics

The overview of trends and challenges in mobile biometrics

Results from MICHE II – Mobile Iris CHallenge Evaluation II

Mobile biometrics represent the new frontier of authentication. The most appealing feature of mobile devices is the wide availability and the presence of more and more reliable sensors for capturing biometric traits, e.g., cameras and accelerometers. Moreover, they more and more often store personal and sensitive data, that need to be protected. Doing this on the same device using biometrics to enforce security seems a natural solution. This makes this research topic attracting and generally promising. However, the growing interest for related applications is counterbalanced by still present limitations, especially for some traits. Acquisition and computation resources are nowadays widely available, but they are not always sufficient to allow a reliable recognition result. Most of all, the way capture is expected to be carried out, i.e., by the user him/herself in uncontrolled conditions and without an expert assistance, can heavily affect the quality of samples and, as a consequence, the accuracy of recognition. Among the biometric traits raising the interest of researchers, iris plays an important role. Mobile Iris CHallenge Evaluation II (MICHE II) competition provided a testbed to assess the progress of mobile iris recognition, as well as its limitations still to overcome. This paper presents the results of the competition and the analysis of achieved performance, that takes into account both proposals submitted for the competition section launched at the 2016 edition of the International Conference on Pattern Recognition (ICPR), as well as proposals submitted for this special issue.

Guest Editorial Special Issue on Mobile Biometrics

Guest Editorial Special Issue on Mobile Biometrics

Authentic mobile‐biometric signature verification system

This is an undeniable fact that in the coming years a considerable percentage of organisations are drifting toward mobile devices for authentication. Banking sector as an additional offshoot has shifted to mobile devices with their applications for e-banking and mobile-banking, giving rise to an emergent requirement of a foolproof and authentic mobile-biometric system. This study presents an authentic mobile-biometric signature verification system and a comparative analysis of the performance of the proposed system for the two datasets; one using the standard device that is used for capturing biometric signatures and the other one is a mobile database taken from a smart phone for biometric signature authentication. The results presented demonstrate that the proposed system outperforms existing mobile-biometric signature verification systems based on dynamic time warping and hidden Markov model. Moreover, this study presents a comprehensive survey of mobile-biometric systems, different devices and hardware needed to support mobile biometrics along with open issues and challenges faced by the mobile-biometric systems. The experiments presented establish that the performance of mobile devices is low as compared with normal biometric signature capturing devices and the major reason the authors found is the absence of pen-tilt angle information in the mobile device datasets.

Banking sector backs biometrics for 2016

The banking sector globally is continuing to throw its weight behind biometric technology for secure authentication. Over 1.1bn financial services customers will be using mobile biometrics by 2020, according to the latest findings of Goode Intelligence.

Trace Attack against Biometric Mobile Applications

With the exponential increase in the dependence on mobile devices in everyday life, there is a growing concern related to privacy and security issues in the Gulf countries; therefore, it is imperative that security threats should be analyzed in detail. Mobile devices store enormous amounts of personal and financial information, unfortunately without any security. In order to secure mobile devices against different threats, biometrics has been applied and shown to be effective. However, biometric mobile applications are also vulnerable to several types of attacks that can decrease their security. Biometric information itself is considered sensitive data; for example, fingerprints can leave traces in touched objects and facial images can be captured everywhere or accessed by the attacker if the facial image is stored in the mobile device (lost or stolen). Hence, an attacker can easily forge the identity of a legitimate user and access data on a device. In this paper, the effects of a trace attack on the sensitivity of biometric mobile applications are investigated in terms of security and user privacy. Experimental results carried out on facial and fingerprint mobile authentication applications using different databases have shown that these mobile applications are vulnerable to the proposed attack, which poses a serious threat to the overall system security and user privacy.

Payment card industry embraces biometric tech

Mobile biometrics will generate $34.6bn in annual revenue in 2020, and will significantly disrupt the global payment market by providing alternative means of securing and processing mobile transactions, according to Acuity Market Intelligence. Biometrics will reduce both the risk and cost of payment processing as Acuity forecasts they will be used to authenticate nearly 65% of all m-commerce transactions in 2020. This represents 126bn biometric payment transactions generating more than $1.1 trillion in consumer m-commerce purchase value.