scholarly journals Continuous Mobile User Authentication Using Combined Biometric Traits

2021 ◽  
Vol 11 (24) ◽  
pp. 11756
Author(s):  
Dominik Reichinger ◽  
Erik Sonnleitner ◽  
Marc Kurz
Keyword(s):  
Mobile Devices ◽  
State Of The Art ◽  
User Authentication ◽  
Single Point ◽  
Mobile User ◽  
Accelerometer Data ◽  
Point Of Entry ◽  
Current State ◽  
Continuous User ◽  
Data Points

Current state of the art authentication systems for mobile devices primarily rely on single point of entry authentication which imposes several flaws. For example, an attacker obtaining an unlocked device can potentially use and exploit it until the screen gets locked again. With continuous mobile user authentication, a system is embedded into the mobile devices, which continuously monitors biometric features of the person using the device, to validate if those monitored inputs match and therefore were made by the previously authenticated user. We start by giving an introduction towards the state of the art of currently used authentication systems and address related problems. For our main contribution we then propose, implement and discuss a continuous user authentication system for the Android ecosystem, which continuously monitors and records touch, accelerometer and timestamp data, and run experiments to gather data from multiple subjects. After feature extraction and normalization, a Hidden Markov Model is employed using an unsupervised learning approach as classifier and integrated into the Android application for further system evaluation and experimentation. The final model achieves an Area Under Curve of up to 100% while maintaining an Equal Error Rate of 1.34%. This is done by combining position and accelerometer data using gestures with at least 50 data points and averaging the prediction result of 25 consecutive gestures.

scholarly journals TKCA: a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lulu Yang ◽  
Chen Li ◽  
Ruibang You ◽  
Bibo Tu ◽  
Linghui Li
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
User Authentication ◽  
Natural Setting ◽  
Short Text ◽  
Current State ◽  
Continuous User ◽  
Series Of Experiments ◽  
Lstm Network ◽  
The Relationship

AbstractKeystroke-based behavioral biometrics have been proven effective for continuous user authentication. Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks. It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings. In this work, we propose a Timely Keystroke-based method for Continuous user Authentication, named TKCA. It integrates the key name and two kinds of timing features through an embedding mechanism. And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. We conduct a series of experiments to validate it on a public dataset - the Clarkson II dataset collected in a completely uncontrolled and natural setting. Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28% of EER when using only 30 keystrokes and 2.78% of EER when using 190 keystrokes.

scholarly journals Soft Threshold Ternary Networks

2020 ◽  
Author(s):  
Weixiang Xu ◽  
Xiangyu He ◽  
Tianli Zhao ◽  
Qinghao Hu ◽  
Peisong Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
State Of The Art ◽  
Performance Gap ◽  
Training Time ◽  
Current State ◽  
The Arts ◽  
Soft Threshold ◽  
And Storage ◽  
Selection Of

Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. In previous ternarized neural networks, a hard threshold Δ is introduced to determine quantization intervals. Although the selection of Δ greatly affects the training results, previous works estimate Δ via an approximation or treat it as a hyper-parameter, which is suboptimal. In this paper, we present the Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine quantization intervals instead of depending on a hard threshold. Concretely, we replace the original ternary kernel with the addition of two binary kernels at training time, where ternary values are determined by the combination of two corresponding binary values. At inference time, we add up the two binary kernels to obtain a single ternary kernel. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and extreme low bit networks. Experiments on ImageNet with AlexNet (Top-1 55.6%), ResNet-18 (Top-1 66.2%) achieves new state-of-the-art.

scholarly journals An Overview of Lidar Imaging Systems for Autonomous Vehicles

2019 ◽  
Vol 9 (19) ◽  
pp. 4093 ◽  
Author(s):  
Santiago Royo ◽  
Maria Ballesta-Garcia
Keyword(s):  
Autonomous Vehicles ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Single Point ◽  
Imaging Systems ◽  
Light Sources ◽  
Final Solution ◽  
Large Uncertainty ◽  
Current State ◽  
The Subject

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

scholarly journals A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3876 ◽  
Author(s):  
Tiantian Zhu ◽  
Zhengqiu Weng ◽  
Guolang Chen ◽  
Lei Fu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Mobile Devices ◽  
Real World ◽  
Large Scale ◽  
User Authentication ◽  
Mobile User ◽  
Learning System ◽  
Support Vector ◽  
Motion Sensors

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

scholarly journals TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5829 ◽  
Author(s):  
Jen-Wei Huang ◽  
Meng-Xun Zhong ◽  
Bijay Prasad Jaysawal
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Data Stream ◽  
State Of The Art ◽  
Streaming Data ◽  
Current State ◽  
Data Points ◽  
Local Outlier ◽  
Time Aware ◽  
Over Time

Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.

A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA

2021 ◽  
Vol 24 (4) ◽  
pp. 1-28
Author(s):  
Abbas Acar ◽  
Shoukat Ali ◽  
Koray Karabina ◽  
Cengiz Kaygusuz ◽  
Hidayet Aksu ◽  
...  
Keyword(s):  
Template Matching ◽  
User Authentication ◽  
Third Party ◽  
Biometric Data ◽  
Concrete System ◽  
Current State ◽  
Continuous Authentication ◽  
User Data ◽  
Continuous User ◽  
Privacy Issues

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, <underline>p</underline>rivacy-<underline>a</underline>ware, and secure <underline>c</underline>ontinuous <underline>a</underline>uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.

Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges

2016 ◽  
Vol 33 (4) ◽  
pp. 49-61 ◽  
Author(s):  
Vishal M. Patel ◽  
Rama Chellappa ◽  
Deepak Chandra ◽  
Brandon Barbello
Keyword(s):  
Mobile Devices ◽  
Recent Progress ◽  
User Authentication ◽  
Continuous User

A Dynamic Model for End Milling Using Single Point Cutting Theory

1996 ◽  
Vol 118 (2) ◽  
pp. 272-274 ◽  
Author(s):  
N. M. Kulkarni ◽  
A. Chandra ◽  
S. S. Jagdale
Keyword(s):  
Process Design ◽  
Dynamic Models ◽  
State Of The Art ◽  
End Milling ◽  
Single Point ◽  
Milling Process ◽  
Current State ◽  
Set Up ◽  
Force Characteristics ◽  
Hierarchy Of Models

The dynamics of a milling process can significantly influence the surface quality and integrity of the finished part. Accordingly, various researchers have investigated the dynamics of milling processes using a hierarchy of models. Tlusty and Smith (1991) provides a review of these models. In recent years, several other researchers (e.g., Armarego and Deshpande, 1989; Montgomery and Altintas, 1991; Nallakatla and Smith, 1992) have also continued to enhance various aspects of such dynamic models. While these dynamic models provide significant insights into the cutting characteristics of a milling process, their utilization in process design has proven to be elusive. The accuracy of these models, however, depends significantly on the prediction of cutting force characteristics. Under the current state-of-the-art, detailed experimentations using actual set-up are necessary to make such predictions accurately. Experimentally obtained constants can vary widely from one milling situation to another, which in turn, significantly restricts their usefulness as predictive tools for process design.

Learning on the Move in the Web 2.0

2013 ◽  
pp. 1693-1714
Author(s):  
Carlos Baladrón ◽  
Javier M. Aguiar ◽  
Lorena Calavia ◽  
Belén Carro ◽  
Antonio Sánchez-Esguevillas
Keyword(s):  
Web 2.0 ◽  
Mobile Devices ◽  
State Of The Art ◽  
New Approach ◽  
Collaborative Tools ◽  
Research And Innovation ◽  
Current State ◽  
Pros And Cons ◽  
Approach To Teaching ◽  
The Web

This work aims at presenting the current state of the art of the m-learning trend, an innovative new approach to teaching focused on taking advantage of mobile devices for learning anytime, anywhere and anyhow, usually employing collaborative tools. However, this new trend is still young, and research and innovation results are still fragmented. This work aims at providing an overview of the state of the art through the analysis of the most interesting initiatives published and reported, studying the different approaches followed, their pros and cons, and their results. And after that, this chapter provides a discussion of where we stand nowadays regarding m-learning, what has been achieved so far, which are the open challenges and where we are heading.

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