Behavioral Biometric Authentication Research Articles

Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

BackgroundObjective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children’s screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation.ObjectiveThe purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement.MethodsWe systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach.ResultsOf the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures (n = 76) and movement (n = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations (n = 34). The overall study quality was low, with an average score of 5.5/14.ConclusionThe field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first.Systematic review registrationThe protocol has been pre-registered in the Open Science Framework database (https://doi.org/10.17605/OSF.IO/92YCT).

Securing the Digital Commerce Spectrum and Cyber Security Strategies for Web, E-commerce, M-commerce, and E-mail Security

Secure protection of sensitive data and financial transactions is of the utmost importance in the dynamic world of online trade. In this study, we present a full-stack security architecture that uses five separate algorithms: ECF, Transaction Anomaly Detection, Adaptive Threat Intelligence, Behavioral Biometric Authentication, and Dynamic Encryption Protocol. By creating encryption keys on the fly while the user logs in, the DEP method lays a solid groundwork for safe data transfer. Behavioral biometric authentication (BBA) uses DEP output to verify users based on their distinct behavior, which is an extra layer of security. By combining both current and past threat information, the ATI algorithm is able to constantly adjust security protocols, providing a preventative shield against new dangers. TAD is an expert at detecting anomalies in online purchases, which helps keep financial transactions honest. When ECF and DEP work together, they filter email content, making communication more secure. Flowcharts help to illustrate the interactions between various algorithms, which helps to understand their operations in detail. Every algorithm's importance is brought to light by an ablation study, which shows how each one contributes and how they all work together to affect the overall security posture. The suggested security framework outperforms the state-of-the-art in terms of efficacy, adaptability, and usability, according to performance evaluations conducted using a number of metrics. These findings can help decision-makers build a strong security plan that is specific to the challenges of online shopping. To conclude, the suggested framework is an integrated and complementary strategy that will strengthen online trade in the face of several cyber dangers while simultaneously protecting the confidentiality, authenticity, and availability of all associated communications and transactions.

IncreAuth: Incremental-Learning-Based Behavioral Biometric Authentication on Smartphones

IncreAuth: Incremental-Learning-Based Behavioral Biometric Authentication on Smartphones

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation.

SnapUnlock: A Contrastive Learning-Based Contactless Authentication via Heterogeneous Sensors

Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.

PushPIN: A Pressure-Based Behavioral Biometric Authentication System for Smartwatches

Smartwatches support diverse applications but suffer from security issues due to their limited resources; their small size poorly supports the rich, accurate input required for screen lock authentication. Additionally, traditional approaches to unlocking smart devices, such as Personal identification number, are highly susceptible to attacks such as guessing and video observation. Therefore, we propose PushPIN, a novel scheme that combines knowledge-based and behavioral biometric approaches to increase security. Input symbols are composed of the selection of one of four different targets with one of five different pressure levels, for a total of 20 possibilities. We complement this passcode by capturing behavioral biometric features from screen touches and wrist motion during input. We present two studies to assess the performance of PushPIN. The first assesses both usability and security against a random guessing attack. It shows acceptable usability—recall times of approximately 8 s and no errors—and strong security: equal error rates of 0.51%. The second study examines the resistance of PushPIN against a video observation attack, ultimately revealing that 36.67% of PushPINs could be cracked, performance that represents a substantial improvement over prior work on pressure-based authentication input. We conclude that pressure-based input can increase the security, while maintaining reasonable usability, of smartwatch lock systems.

A Study of Touch Dynamics Biometrics Authentication

A lot of recent research activities have been fulfilled in keystroke and mouse dynamics in the past three decades but the work in touch screen biometric authentication still needs to be explored. The motivation of this paper is that touch dynamics behavioral biometric authentication can be easily deployed along with user normal activities. Behavioral biometric along with machine learning algorithms and risk-assessment techniques can be efficiently and effectively used as an adaptive authentication. With the rapid usage of smart phones and touch screens in other applications, touch dynamics can be an important authentication factor in multi-factor authentication. Though a numerous studies have been done in behavioral human biometric, there is indeed much scope in the area of touch dynamics. The objective of this study is to provide the throughout survey of techniques used in this field along with the advancements for further research work. Keywords Biometric, touch dynamics, Behavioral authentication, multi-factor authentication, adaptive authentication

VOICE BIOMETRIC IDENTITY AUTHENTICATION MODEL FOR IOT DEVICES

Behavioral bio-metric authentication is considered as a promising approach to securing the internet of things (IoT) ecosystem. In this paper, we investigated the need and suitability of employing voice recognition systems in the user authentication of the IoT. Tools and techniques used in accomplishing voice recognition systems are reviewed, and their appropriateness to the IoT environment are discussed. In the end, a voice recognition system is proposed for IoT ecosystem user authentication. The proposed system has two phases. The first being the enrollment phase consisting of a pre-processing step where the noise is removed from the voice for the enrollment process, the feature extraction step where feature traits are extracted from user’s voice, and the model training step where the voice model is trained for the IoT user. And the second being the phase verifies whether the identity claimer is the owner of the IoT device. Based on the resources limitedness of the IoT technologies, the suitability of text-dependent voice recognition systems is promoted. Likewise, the use of MFCC features is considered in the proposed system.

BAMHealthCloud: A biometric authentication and data management system for healthcare data in cloud

Advancements in the healthcare industry have given rise to the security threat to the ever growing e-medical data. The healthcare data management system records patient’s data in different formats such as text, numeric, pictures and videos leading to data which is big and unstructured. Also, hospitals may have several branches in different geographical locations. Sometimes, for research purposes, there is a need to integrate patients’ health data stored at different locations. In view of this, a cloud-based healthcare management system can be an effective solution for efficient health care data management. But the major concern of cloud-based healthcare system is the security aspect. It includes theft of identity, tax fraudulence, bank fraud, insurance frauds, medical frauds and defamation of high profile patients. Hence, a secure data access and retrieval is needed in order to provide security of critical medical records in healthcare management system. Biometric based authentication mechanism is suitable in this scenario since it overcomes the limitations of token theft and forgetting passwords in the conventional token id-password mechanism used for providing security. It also has high accuracy rate for secure data access and retrieval. In the present paper, a cloud-based system for management of healthcare data BAMHealthCloud is proposed, which ensures the security of e-medical data access through a behavioral biometric signature-based authentication. Training of the signature samples for authentication purpose has been performed in parallel on Hadoop MapReduce framework using Resilient Backpropagation neural network. From rigorous experiments, it can be concluded that it achieves a speedup of 9 times, Equal error rate (EER) of 0.12, the sensitivity of 0.98 and specificity of 0.95. Performance comparison of the system with other state-of-art-algorithms shows that the proposed system preforms better than the existing systems in literature.

SegAuth: A Segment-based Approach to Behavioral Biometric Authentication.

Many studies have been conducted to apply behavioral biometric authentication on/with mobile devices and they have shown promising results. However, the concern about the verification accuracy of behavioral biometrics is still common given the dynamic nature of behavioral biometrics. In this paper, we address the accuracy concern from a new perspective-behavior segments, that is, segments of a gesture instead of the whole gesture as the basic building block for behavioral biometric authentication. With this unique perspective, we propose a new behavioral biometric authentication method called SegAuth, which can be applied to various gesture or motion based authentication scenarios. SegAuth can achieve high accuracy by focusing on each user's distinctive gesture segments that frequently appear across his or her gestures. In SegAuth, a time series derived from a gesture/motion is first partitioned into segments and then transformed into a set of string tokens in which the tokens representing distinctive, repetitive segments are associated with higher genuine probabilities than those tokens that are common across users. An overall genuine score calculated from all the tokens derived from a gesture is used to determine the user's authenticity. We have assessed the effectiveness of SegAuth using 4 different datasets. Our experimental results demonstrate that SegAuth can achieve higher accuracy consistently than existing popular methods on the evaluation datasets.

User Authentication based on Dynamic Keystroke Recognition

Biometric identification is a very good candidate technology, which can facilitate a trusted user authentication with minimum constraints on the security of the access point. However, most of the biometric identification techniques require special hardware, thus complicate the access point and make it costly. Keystroke recognition is a biometric identification technique which relies on the user behavior while typing on the keyboard. It is a more secure and does not need any additional hardware to the access point. This paper presents a developed behavioral biometric authentication method which enables to identify the user based on his Keystroke Static Authentication (KSA) and describes an authentication system that explains the ability of keystroke technique to authenticate the user based on his template profile saved in the database. Also, an algorithm based on dynamic keystroke analysis has been presented, synthesized, simulated and implemented on Field Programmable Gate Array (FPGA). The proposed algorithm is tested on 25 individuals, achieving a False Rejection Rate (FRR) about 4% and a False Acceptance Rate (FAR) about 0%. This performance is reached using the same sampling text for all the individuals. In this paper, two methods are used to implement the proposed approach: method one (H/W based Sorter) and method two (S/W based Sorter) are achieved execution time about 50.653 ns and 9.650 ns, respectively. Method two achieved a lower execution time; the time in which the proposed algorithm is executed on FPGA board, compared to some published results. As the second method achieved a small execution time and area utilization so it is the preferred method to be implemented on FPGA.

User identification and authentication using multi-modal behavioral biometrics

Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are worse than more traditional authentication methods. This article presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user's computer activity while requiring less user interaction to train the system than previous work. Testing over 31 users shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Between the two fusion techniques presented, feature fusion and an ensemble based classification method, the ensemble method performs the best with a False Acceptance Rate (FAR) of 2.10% and a False Rejection Rate (FRR) 2.24%.

Keystroke Dynamic Authentication in Mobile Cloud Computing

Contemporary mobile sets are not used just for making calls and sending messages. They are increasingly being used in Mobile Cloud Computing (MCC) to store sensitive and critical information as well as to access sensitive data using the Internet via Cloud Service Provider (CSP). MCC is combination of Cloud Computing (CC) and mobile communication. Using MCC helps to decrease running cost and expansion of mobile applications. One of the important challenges in MCC is security and privacy. Furthermore, authentication plays an important role in preserving security and privacy of shared information in MCC. In fact, the majority of mobile handsets use inherently weak authentication mechanisms, based upon passwords and Personal Identification Number(PINs). But it is not secure way for authenticating users because of its limitation, as well as it is difficult to confirm that the demand is from the rightful owner. This study focus on a type of behavioral biometric authentication that is called Keystroke Dynamic Authentication (KDA) to identify mobile users and improve the authentication mechanism in cloud server. There are different parameters of measuring keystrokes, we defined keystrokes duration as an attribute to identify user. For implementing this method, we use Android SDK development. It includes mobile device emulator, Android Virtual Device (AVD) that helps to develop and test Android applications without using a physical device. Test of the the application is based on three different approaches (client side, local server, cloud server).In the first approach, experimental results is obtained from mobile device (client side) shows that this method works 94% correctly. In the second approach, application is connected to the php server and obtained results show that the application can works 96:15%correctly. In the last approach, obtained results from cloud server (google drive) show that the application can work 95:34% correctly. The important point in these approaches is that, the username and password were known for people cooperate in testing the proposed application. In addition, KDA is based on unique characteristics of users (here keystrokes duration) and it is hard to pretend as legible user. Therefore, applying KDA in mobile set helps to improve security and privacy of authentication.