Non-intrusive Authentication Research Articles

M-PPG: A Nonintrusive Authentication for Wearable Devices via PPG Sensors With Motor Excitations

M-PPG: A Nonintrusive Authentication for Wearable Devices via PPG Sensors With Motor Excitations

A novel speaker verification approach featuring multidomain acoustics based on the weighted city block Minkowski distance

AbstractAccess control is vital in interconnected environments like the Internet of Things, Industry 4.0, and smart connectivity, ensuring authorized access for security. Biometric‐based access, particularly speaker verification (SV), enhances security with unique vocal features, offering nonintrusive authentication with continuous monitoring. Single‐domain features prove insufficient in distinguishing similar traits, prompting latest SV advancements to adopt multidomain‐based speech features. This paradigm addresses the limitations of single‐domain features by amalgamating the merits of individual domains, establishing a cutting‐edge approach. It utilizes cepstral–frequency–time domain feature fusion, achieved via cepstral mean‐variance normalization for generalizability. The weighted city block Minkowski distance is proposed to compare reference and test speech templates. Parameters are computed based on the confusion matrix, template matching distance functions, dynamic acoustic conditions, and additive white Gaussian noise. A deep convolutional neural network classifier is assessed on open‐source LibriSpeech and Speaker in the Wild corpora, surpassing the current methodologies.

PALOT: Profiling and Authenticating Users Leveraging Internet of Things.

Continuous authentication was introduced to propose novel mechanisms to validate users’ identity and address the problems and limitations exposed by traditional techniques. However, this methodology poses several challenges that remain unsolved. In this paper, we present a novel framework, PALOT, that leverages IoT to provide context-aware, continuous and non-intrusive authentication and authorization services. To this end, we propose a formal information system model based on ontologies, representing the main source of knowledge of our framework. Furthermore, to recognize users’ behavioral patterns within the IoT ecosystem, we introduced a new module called “confidence manager”. The module is then integrated into an extended version of our early framework architecture, IoTCAF, which is consequently adapted to include the above-mentioned component. Exhaustive experiments demonstrated the efficacy, feasibility and scalability of the proposed solution.

Kinect User Authentication using Multimodal Biometric

Objectives: Facial Recognition system has been the most popular non-intrusive authentication system. The proposed system offers to improve the accuracy of facial recognition using multimodal authentication with a 3d depth sensor. Methods/Statistical Analysis: To increase the reliability and robust of the system, we superimpose skeletal biometric features with facial features where data combined using 3D Imaging Technology. Statistical approaches are used for feature transformation using PCA and LDA Algorithm. Findings: To evaluate the system, we collected nearly 1000 individuals with two samples for each, captured in indoor environments under various light effects. The accuracy of the system is built with 95% confidence interval and based on a classifier, three performance curves Receiver Operating Characteristics (ROC), Cumulative Match Score Curves (CMC) and Expected Performance Curves (EPC) are used for performance evaluation. The FRR of the proposed multimodal fusion approach is 0.12 at 0.001 minimal FAR with 95% accuracy. Applications/Improvements: Multimodal biometric is required for strong authentication for the surveillance of smart home, cities and health care sectors where security plays a vital role. This fusion technique improves the reliability, latency, and accuracy of the system in recognizing and authenticating the user. Keywords: 3D Imaging Technology, Face Features, Multimodal Authentication, Skeletal Biometric

A novel nonintrusive user authentication method based on touch gestures for smartphones

Novel nonintrusive authentication mechanisms for smartphones have recently been investigated to complement existing protection mechanisms. A major reason is that a nonintrusive approach can continually authenticate users without interrupting their operations. In this paper, a novel approach to nonintrusive authentication of smartphone users involving flick gestures is proposed. Sixteen histogram-based features of flick gestures, including five novel features related to the habitual touch positions of a user, were used for authentication. Empirical results based on 51 participants indicated that the proposed approach was feasible. The equal error rate of the proposed approach was approximately 4.37% ~ 5.13% when the histogram-based features were calculated based on 30 flicks and decreased to approximately 2.35% ~ 2.99% for features based on 60 flicks. In addition, the experimental results revealed that the five novel features proposed are effective. Possible applications and limitations of the proposed approach are discussed.

Design and Analysis of a Highly User-Friendly, Secure, Privacy-Preserving, and Revocable Authentication Method

A large portion of system breaches are caused by authentication failure, either during the login process or in the post-authentication session; these failures are themselves related to the limitations associated with existing authentication methods. Current authentication methods, whether proxy based or biometrics based, are not user-centric and/or endanger users' (biometric) security and privacy. In this paper, we propose a biometrics based user-centric authentication approach. This method involves introducing a reference subject (RS), securely fusing the user's biometrics with the RS, generating a BioCapsule (BC) from the fused biometrics, and employing BCs for authentication. Such an approach is user friendly, identity bearing yet privacy-preserving, resilient, and revocable once a BC is compromised. It also supports “one-click sign-on” across systems by fusing the user's biometrics with a distinct RS on each system. Moreover, active and non-intrusive authentication can be automatically performed during post-authentication sessions. We formally prove that the secure fusion based approach is secure against various attacks. Extensive experiments and detailed comparison with existing approaches show that its performance (i.e., authentication accuracy) is comparable to existing typical biometric approaches and the new BC based approach also possesses many desirable features such as diversity and revocability.

A Preliminary Study on Non-Intrusive User Authentication Method Using Smartphone Sensors

This paper proposes a non-intrusive authentication method based on two sensitive apparatus of smartphones, namely, the orientation sensor and the touchscreen. We have found that these two sensors are capable of capturing behavioral biometrics of a user while the user is engaged in relatively stationary activities. The experimental results with respect to two types of flick operating have an equal error rate of about 3.5% and 5%, respectively. To the best of our knowledge, this work is the first publicly reported study that simultaneously adopts the orientation sensor and the touchscreen to build an authentication model for smartphone users. Finally, we show that the proposed approach can be used together with existing intrusive mechanisms, such as password and/or fingerprints, to build a more robust authentication framework for smartphone users.