SenCS

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Chaohao Li ◽  
Xiaoyu Ji ◽  
Bin Wang ◽  
Kai Wang ◽  
Wenyuan Xu
Keyword(s):  
Mobile Devices ◽  
Error Rate ◽  
Proof Of Concept ◽  
Equal Error Rate ◽  
Time Intervals ◽  
Heterogeneous Sensors ◽  
Verification Scheme ◽  
Hotel Room ◽  
Human Interactions ◽  
Time Autonomous

Indoor proximity verification has become an increasingly useful primitive for the scenarios where access is granted to the previously unknown users when they enter a given area (e.g., a hotel room). Existing solutions either rely on homogeneous sensing modalities shared by two parties or require additional human interactions. In this article, we propose a context-based indoor proximity verification scheme, called SenCS, to enable real-time autonomous access for mobile devices, utilizing the available heterogeneous sensors at the user side and at the room side. The intuition is that only when the user is within a room can sensors from both sides observe the same events in the room. Yet such a solution is challenging, because the events may not provide enough entropy within the required time and the heterogeneity in sensing modalities may not always agree on the sensed events. To overcome the challenges, we exploit the time intervals between successively human actions to create heterogeneous contextual fingerprints (HCF) at a millisecond level. By comparing the contextual similarity between the HCF s from both the room and user sides, SenCS accomplishes the indoor proximity verification. Through proof-of-concept implementation and evaluations on 30 participants, SenCS achieves an accuracy of 99.77% and an equal error rate (EER) of 0.23% across various hardware configurations.

Misuse Detection for Mobile Devices Using Behaviour Profiling

2011 ◽  
Vol 1 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Fudong Li ◽  
Nathan Clarke ◽  
Maria Papadaki ◽  
Paul Dowland
Keyword(s):  
Mobile Devices ◽  
Error Rate ◽  
Text Message ◽  
Modern Society ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Misuse Detection ◽  
General Application ◽  
Point Of Entry ◽  
Application Specific

Mobile devices have become essential to modern society; however, as their popularity has grown, so has the requirement to ensure devices remain secure. This paper proposes a behaviour-based profiling technique using a mobile user’s application usage to detect abnormal activities. Through operating transparently to the user, the approach offers significant advantages over traditional point-of-entry authentication and can provide continuous protection. The experiment employed the MIT Reality dataset and a total of 45,529 log entries. Four experiments were devised based on an application-level dataset containing the general application; two application-specific datasets combined with telephony and text message data; and a combined dataset that included both application-level and application-specific. Based on the experiments, a user’s profile was built using either static or dynamic profiles and the best experimental results for the application-level applications, telephone, text message, and multi-instance applications were an EER (Equal Error Rate) of 13.5%, 5.4%, 2.2%, and 10%, respectively.

scholarly journals Building Standardized and Secure Mobile Health Services Based on Social Media

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2208
Author(s):  
Jesús D. Trigo ◽  
Óscar J. Rubio ◽  
Miguel Martínez-Espronceda ◽  
Álvaro Alesanco ◽  
José García ◽  
...  
Keyword(s):  
Social Media ◽  
Mobile Devices ◽  
Mobile Health ◽  
Healthcare Services ◽  
Small Scale ◽  
Proof Of Concept ◽  
Privacy And Security ◽  
Health Level 7 ◽  
Information Embedding ◽  
Android Applications

Mobile devices and social media have been used to create empowering healthcare services. However, privacy and security concerns remain. Furthermore, the integration of interoperability biomedical standards is a strategic feature. Thus, the objective of this paper is to build enhanced healthcare services by merging all these components. Methodologically, the current mobile health telemonitoring architectures and their limitations are described, leading to the identification of new potentialities for a novel architecture. As a result, a standardized, secure/private, social-media-based mobile health architecture has been proposed and discussed. Additionally, a technical proof-of-concept (two Android applications) has been developed by selecting a social media (Twitter), a security envelope (open Pretty Good Privacy (openPGP)), a standard (Health Level 7 (HL7)) and an information-embedding algorithm (modifying the transparency channel, with two versions). The tests performed included a small-scale and a boundary scenario. For the former, two sizes of images were tested; for the latter, the two versions of the embedding algorithm were tested. The results show that the system is fast enough (less than 1 s) for most mHealth telemonitoring services. The architecture provides users with friendly (images shared via social media), straightforward (fast and inexpensive), secure/private and interoperable mHealth services.

scholarly journals A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

2020 ◽  
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Arokia Nathan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Error Rate ◽  
User Authentication ◽  
The Internet ◽  
Equal Error Rate ◽  
High User ◽  
Iot Applications ◽  
The Internet Of Things

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

scholarly journals The phone walkers: a study of human dependence on inactive mobile devices

Behaviour ◽  
2018 ◽  
Vol 155 (5) ◽  
pp. 389-414 ◽  
Author(s):  
Laura P. Schaposnik ◽  
James Unwin
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Census Data ◽  
Social Groups ◽  
Clear Pattern ◽  
Clear Tendency ◽  
Human Interactions ◽  
Opposite Sex ◽  
Significant Attention

Abstract The development of mobile phones has largely increased human interactions. Whilst the use of these devices for communication has received significant attention, there has been little analysis of more passive interactions. Through census data on casual social groups, this work suggests a clear pattern of mobile phones being carried in people’s hands, without the person using it (that is, not looking at it). Moreover, this study suggests that when individuals join members of the opposite sex there is a clear tendency to stop holding mobile phones whilst walking. Although it is not clear why people hold their phones whilst walking in such large proportions (38% of solitary women, and 31% of solitary men), we highlight several possible explanation for holding the device, including the need to advertise status and affluence, to maintain immediate connection with friends and family, and to mitigate feelings related to anxiety and security.

Using the Wii Remote for Mobile Device Application Testing

2015 ◽  
Vol 6 (1) ◽  
pp. 34-43
Author(s):  
Mark Bruce Freeman
Keyword(s):  
Mobile Devices ◽  
User Experience ◽  
Development Process ◽  
Primary Source ◽  
Proof Of Concept ◽  
Application Development ◽  
The Past ◽  
Phone Calls ◽  
Innovative Method ◽  
Dramatic Shift

There has been a dramatic shift in the interaction methods of mobile devices over the past decade. From devices simply being able to make phone calls to being able to handle complex tasks traditionally performed on personal computers (PCs); this change has led to new interaction issues that need to be understood during the application development process, particularly as these devices now commonly incorporate a touch-screen as their primary source of input. Currently, the methods of conducting software user experience testing of these devices employs techniques that were developed for PCs, however mobile devices are used within different contexts of use. This research initially reviews the current methods for user experience testing of applications running on mobile devices and then presents, through a proof-of-concept approach, an innovative method for conducting user experience testing employing actual devices.

scholarly journals Implicit Secondary Authentication for Sustainable SMS Authentication

2019 ◽  
Vol 11 (1) ◽  
pp. 279
Author(s):  
Gwonsang Ryu ◽  
Seung-Hyun Kim ◽  
Daeseon Choi
Keyword(s):  
Error Rate ◽  
Access Point ◽  
Environmental Information ◽  
Behavioral Pattern ◽  
Environmental Data ◽  
Smart Device ◽  
Short Message ◽  
Equal Error Rate ◽  
Smart Watch ◽  
Message Service

Short message service (SMS) is the most widely adopted multi-factor authentication method for consumer-facing accounts. However, SMS authentication is susceptible to vulnerabilities such as man-in-the-middle attack, smishing, and device theft. This study proposes implicit authentication based on behavioral pattern of users when they check an SMS verification code and environmental information of user proximity to detect device theft. User behavioral pattern is collected by using the accelerometer and gyroscope of a smart device such as a smartphone and smart watch. User environmental information is collected using device fingerprint, wireless access point, Bluetooth, and global positioning system information. To evaluate the performance of the proposed scheme, we perform experiments using a total of 1320 behavioral and environmental data collected from 22 participants. The scheme achieves an average equal error rate of 6.27% when using both behavioral and environmental data collected from only a smartphone. Moreover, it achieves an average equal error rate of 0% when using both behavioral and environmental data collected from a smartphone and smart watch. Therefore, the proposed scheme can be employed for more secure SMS authentication.

scholarly journals A Security Monitoring Framework for Mobile Devices

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1197 ◽  
Author(s):  
António Lima ◽  
Luis Rosa ◽  
Tiago Cruz ◽  
Paulo Simões
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Lessons Learned ◽  
Bring Your Own Device ◽  
Reference Architecture ◽  
Proof Of Concept ◽  
Security Monitoring ◽  
Detection Systems ◽  
Management Policies ◽  
Computing Platforms

Quite often, organizations are confronted with the burden of managing mobile device assets, requiring control over installed applications, security, usage profiles or customization options. From this perspective, the emergence of the Bring Your Own Device (BYOD) trend has aggravated the situation, making it difficult to achieve an adequate balance between corporate regulations, freedom of usage and device heterogeneity. Moreover, device and information protection on mobile ecosystems are quite different from securing other device assets such as laptops or desktops, due to their specific characteristics and limitations—quite often, the resource overhead associated with specific security mechanisms is more important for mobile devices than conventional computing platforms, as the former frequently have comparatively less computing capabilities and more strict power management policies. This paper presents an intrusion and anomaly detection framework specifically designed for managed mobile device ecosystems, that is able to integrate into mobile device and management frameworks for complementing conventional intrusion detection systems. In addition to presenting the reference architecture for the proposed framework, several implementation aspects are also analyzed, based on the lessons learned from developing a proof-of-concept prototype that was used for validation purposes.

Detecção de Anomalias em Vias Públicas Usando Características Espaciais e um Classificador Sequencial Bidirecional

2020 ◽  
Author(s):  
Fábio Ricardo Oliveira Bento ◽  
Raquel Frizera Vassallo ◽  
Jorge Leonid Aching Samatelo
Keyword(s):  
Anomaly Detection ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Error Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Equal Error Rate ◽  
Operating Characteristic Curve ◽  
End To End ◽  
Recall Curve

Detecção de anomalias consiste na identificação de eventos que não estão em conformidade com um padrão de comportamento esperado. No contexto de segurança em vias públicas, a detecção automática de eventos anômalos através de video, tem aplicação na identificação de comportamentos suspeitos. Nesse artigo é proposta uma abordagem para o problema da detecção automática de eventos anômalos em vı́deos de vias públicas baseado em um modelo de redes neurais profundas end-to-end, composto de duas partes: um extrator de caracterı́sticas espaciais baseado em uma rede neural convolucional pre-treinada, e um classificador de sequências temporais baseado em camadas recorrentes empilhadas. Realizamos experimentos nos conjuntos de dados UCSD Anomaly Detection Dataset. Os resultados foram avaliados com as métricas Area Under the Receiver Operating Characteristic Curve - AUC, Area Under the Precision vs Recall Curve - AUPRC e Equal Error Rate – EER. Durante os experimentos, o modelo obteve AUC acima de 95% e EER abaixo de 9%, os quais são resultados compatı́veis com a literatura atual.

scholarly journals Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Margit Antal ◽  
László Zsolt Szabó ◽  
Tünde Tordai
Keyword(s):  
Error Rate ◽  
Error Rates ◽  
Equal Error Rate ◽  
Global Features ◽  
Evaluation Protocol ◽  
Global Threshold ◽  
Online Signature ◽  
Feature Based ◽  
Basic Quality

We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. The database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. The database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. The lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. The same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. The results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.

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