NomadiKey: User authentication for smart devices based on nomadic keys

2017 ◽  
Vol 28 (1) ◽  
pp. e1998 ◽  
Author(s):  
Artur Souza ◽  
Ítalo Cunha ◽  
Leonardo B Oliveira
Keyword(s):  
User Authentication ◽  
Smart Devices

scholarly journals A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4212
Author(s):  
Priscila Morais Argôlo Bonfim Estrela ◽  
Robson de Oliveira Albuquerque ◽  
Dino Macedo Amaral ◽  
William Ferreira Giozza ◽  
Rafael Timóteo de Sousa Júnior
Keyword(s):  
User Authentication ◽  
Internet Banking ◽  
Supervised Machine Learning ◽  
Smart Devices ◽  
Static Verification ◽  
User Behaviors ◽  
Legitimate User ◽  
Continuous User ◽  
Mobile Banking Applications ◽  
Effective Layer

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.

scholarly journals Biometrics in cyber defense

2020 ◽  
Vol 309 ◽  
pp. 02003
Author(s):  
Gabriela Mogos
Keyword(s):  
Literature Review ◽  
Iris Recognition ◽  
User Authentication ◽  
Smart Devices ◽  
Biometric Identification ◽  
Cyber Defense ◽  
Security Risks ◽  
Strong Case ◽  
Current Practices

Biometric identification is an up and coming authentication method. The growing complexity of and overlap between smart devices, usability patterns and security risks make a strong case for securer and safer user authentication. This paper aims to offer a broad literature review on iris recognition and biometric cryptography to better understand current practices, propose possible future enhancements and anticipate possible future usability and security developments.

scholarly journals Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Farman Pirzado ◽  
Shahzad Memon ◽  
Lachman Das Dhomeja Dhomeja ◽  
Awais Ahmed
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Mobile Banking ◽  
Alternative Technique ◽  
Keystroke Dynamics ◽  
Data Set ◽  
Proposed Model

Nowadays, smart devices have become a part of ourlives, hold our data, and are used for sensitive transactions likeinternet banking, mobile banking, etc. Therefore, it is crucial tosecure the data in these smart devices from theft or misplacement.The majority of the devices are secured with password/PINbaseduser authentication methods, which are already proveda less secure or easily guessable user authentication method.An alternative technique for securing smart devices is keystrokedynamics. Keystroke dynamics (KSD) is behavioral biometrics,which uses a natural typing pattern unique in every individualand difficult to fake or replicates that pattern. This paperproposes a user authentication model based on KSD as an additionalsecurity method for increasing the smart devices’ securitylevel. In order to analyze the proposed model, an android-basedapplication has been implemented for collecting data from fakeand genuine users. Six machine learning algorithms have beentested on the collected data set to study their suitability for usein the keystroke dynamics-based authentication model.

scholarly journals ChestLive

2021 ◽  
Vol 5 (4) ◽  
pp. 1-25
Author(s):  
Yanjiao Chen ◽  
Meng Xue ◽  
Jian Zhang ◽  
Qianyun Guan ◽  
Zhiyuan Wang ◽  
...  
Keyword(s):  
User Authentication ◽  
Smart Devices ◽  
Fine Grained ◽  
Voice Signal ◽  
Motion Signal ◽  
Replay Attacks ◽  
Training Samples ◽  
Meta Learning ◽  
In The Wild ◽  
Working Distance

Voice-based authentication is prevalent on smart devices to verify the legitimacy of users, but is vulnerable to replay attacks. In this paper, we propose to leverage the distinctive chest motions during speaking to establish a secure multi-factor authentication system, named ChestLive. Compared with other biometric-based authentication systems, ChestLive does not require users to remember any complicated information (e.g., hand gestures, doodles) and the working distance is much longer (30cm). We use acoustic sensing to monitor chest motions with a built-in speaker and microphone on smartphones. To obtain fine-grained chest motion signals during speaking for reliable user authentication, we derive Channel Energy (CE) of acoustic signals to capture the chest movement, and then remove the static and non-static interference from the aggregated CE signals. Representative features are extracted from the correlation between voice signal and corresponding chest motion signal. Unlike learning-based image or speech recognition models with millions of available training samples, our system needs to deal with a limited number of samples from legitimate users during enrollment. To address this problem, we resort to meta-learning, which initializes a general model with good generalization property that can be quickly fine-tuned to identify a new user. We implement ChestLive as an application and evaluate its performance in the wild with 61 volunteers using their smartphones. Experiment results show that ChestLive achieves an authentication accuracy of 98.31% and less than 2% of false accept rate against replay attacks and impersonation attacks. We also validate that ChestLive is robust to various factors, including training set size, distance, angle, posture, phone models, and environment noises.

scholarly journals ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2466 ◽  
Author(s):  
Maryam Naseer Malik ◽  
Muhammad Awais Azam ◽  
Muhammad Ehatisham-Ul-Haq ◽  
Waleed Ejaz ◽  
Asra Khalid
Keyword(s):  
Internet Of Things ◽  
User Authentication ◽  
Smart Cities ◽  
Activity Patterns ◽  
Wearable Sensors ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Healthcare Applications ◽  
The Internet Of Things

The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users’ privacy when interacting with smart devices. This paper proposes a heterogeneous framework “ADLAuth” for passive and implicit authentication of the user using either a smartphone’s built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users’ identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.

scholarly journals Efficient encrypted image retrieval in IoT-cloud with multi-user authentication

2018 ◽  
Vol 14 (2) ◽  
pp. 155014771876181 ◽  
Author(s):  
Mustafa A Al Sibahee ◽  
Songfeng Lu ◽  
Zaid Ameen Abduljabbar ◽  
Ayad Ibrahim ◽  
Zaid Alaa Hussien ◽  
...  
Keyword(s):  
Euclidean Distance ◽  
User Authentication ◽  
Locality Sensitive Hashing ◽  
Security And Privacy ◽  
Smart Devices ◽  
Encrypted Data ◽  
Minimum Number ◽  
Search Speed ◽  
Encrypted Images ◽  
And Storage

Encryption is one of the best methods to safeguard the security and privacy of an image. However, looking through encrypted data is difficult. A number of techniques for searching encrypted data have been devised. However, certain security solutions may not be used in smart devices in IoT-cloud because such solutions are not lightweight. In this article, we present a lightweight scheme that can enable a content-based search through encrypted images. In particular, images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two feature vectors. In addition, we use a hashing method, namely, locality-sensitive hashing, to devise the searchable index. The use of an locality-sensitive hashing index increases the proficiency and effectiveness of a system, thereby allowing the retrieval of only relevant images with a minimum number of distance evaluations. Refining vector techniques are used to refine relevant results efficiently and securely. Our index construction process ensures that stored data and trapdoors are kept private. Our system also efficiently supports multi-user authentication by avoiding the expensive traditional method, which enables data owners to define who can search for a specific image. Compared with other similarity-based encryption methods predicated upon searchability, the option presented in this study offers superior search speed and storage efficiency.

Sonicprint

2021 ◽  
Vol 24 (4) ◽  
pp. 43-46
Author(s):  
Aditya Singh Rathore ◽  
Chenhan Xu ◽  
Wenyao Xu
Keyword(s):  
User Authentication ◽  
Low Cost ◽  
Form Factors ◽  
Smart Devices ◽  
Smart Device ◽  
Great Promise ◽  
Unique Form ◽  
Passive Sensing ◽  
Fingerprint Biometrics ◽  
Specialized Hardware

Although fingerprint technology holds great promise for user authentication, commercial scanners face significant challenges in terms of security (e.g., fake finger) and adoptability (e.g., wearables). SonicPrint pushes the boundary of fingerprint biometrics beyond smartphones to any smart devices without the need for specialized hardware. To achieve this, it listens for fingerprintinduced sonic effect (FiSe) caused when a user swipes his/her fingertip on smart device surface. Compared to other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacyoriented and secure approach to identify users across smart devices of unique form-factors.

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