Using Deep Learning to Identify Security Risks of Personal Mobile Devices in Enterprise Networks

2020 ◽  
Author(s):  
Lanier Watkins ◽  
Yue Yu ◽  
Sifan Li ◽  
William H. Robinson ◽  
Aviel Rubin
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Enterprise Networks ◽  
Security Risks ◽  
Personal Mobile Devices

Secure Examination Management System for M-Learning (SEMS)

2017 ◽  
Vol 2 (2) ◽  
pp. 31-35
Author(s):  
Akshada Abnave ◽  
Charulata Banait ◽  
Mrunalini Chopade ◽  
Supriya Godalkar ◽  
Soudamini Pawar ◽  
...  
Keyword(s):  
Face Recognition ◽  
Social Interactions ◽  
Mobile Devices ◽  
Management System ◽  
Mobile Apps ◽  
Smart Phones ◽  
Examination System ◽  
Open Environment ◽  
Exchange Information ◽  
Personal Mobile Devices

M-learning or mobile learning is defined as learning through mobile apps, social interactions and online educational hubs via Internet or network using personal mobile devices such as tablets and smart phones. However, in such open environment examination security is most challenging task as students can exchange mobile devices or also can exchange information through network during examination. This paper aims to design secure examination management system for m- learning and provide appropriate mechanism for anti- impersonation to ensure examination security. The users are authenticated through OTP. To prevent students from exchanging mobile devices during examination, system re-authenticates students automatically through face recognition at random time without interrupting the test. The system also provides external click management i.e. prevent students from accessing online sites and already downloaded files during examination.

scholarly journals Contact Tracing: a game of big numbers in the time of COVID-19

2020 ◽  
Author(s):  
Hyunju Kim ◽  
Ayan Paul
Keyword(s):  
Mobile Devices ◽  
Optimal Solution ◽  
Social Structures ◽  
Contact Tracing ◽  
Potential Danger ◽  
Crucial Importance ◽  
Minimum Threshold ◽  
Proximity Data ◽  
Slowing Down ◽  
Personal Mobile Devices

ABSTRACTOne of the more widely advocated solutions to slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for contact tracing as this provides a major gain over a manual implementation. In this work, we study the characteristics of automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work for the current pandemic. We display the vulnerabilities of the strategy to inadequately sample the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that a strong reliance on contact tracing to contain the spread of the SARS-CoV-2 pandemic can lead to the potential danger of allowing the pandemic to spread unchecked. A carefully thought out strategy for controlling the spread of the pandemic along with automated contact tracing can lead to an optimal solution.

scholarly journals Using mobile devices for inpatient rounding and handoffs: an innovative application developed and rapidly adopted by clinicians in a pediatric hospital

2016 ◽  
Vol 24 (e1) ◽  
pp. e69-e78 ◽  
Author(s):  
Aude Motulsky ◽  
Jenna Wong ◽  
Jean-Pierre Cordeau ◽  
Jorge Pomalaza ◽  
Jeffrey Barkun ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Neonatal Intensive Care ◽  
Hospital Setting ◽  
Data Entry ◽  
Bring Your Own Device ◽  
Neonatal Intensive Care Units ◽  
Desktop Computers ◽  
Local Practices ◽  
Usage Patterns ◽  
Personal Mobile Devices

Objective: To describe the usage of a novel application (The FLOW) that allows mobile devices to be used for rounding and handoffs. Materials and Methods: The FLOW provides a view of patient data and the capacity to enter short notes via personal mobile devices. It was deployed using a “bring-your-own-device” model in 4 pilot units. Social network analysis (SNA) was applied to audit trails in order to visualize usage patterns. A questionnaire was used to describe user experience. Results: Overall, 253 health professionals used The FLOW with their personal mobile devices from October 2013 to March 2015. In pediatric and neonatal intensive care units (ICUs), a median of 26–26.5 notes were entered per user per day. Visual network representation of app entries showed that usage patterns were different between the ICUs. In 127 questionnaires (50%), respondents reported using The FLOW most often to enter notes and for handoffs. The FLOW was perceived as having improved patient care by 57% of respondents, compared to usual care. Most respondents (86%) wished to continue using The FLOW. Discussion: This study shows how a handoff and rounding tool was quickly adopted in pediatric and neonatal ICUs in a hospital setting where patient charts were still paper-based. Originally developed as a tool to support informal documentation using smartphones, it was adapted to local practices and expanded to print sign-out documents and import notes within the medicolegal record with desktop computers. Interestingly, even if not supported by the nursing administrative authorities, the level of use for data entry among nurses and doctors was similar in all units, indicating close collaboration in documentation practices in these ICUs.

Mobile Devices in Higher Education Classrooms

2015 ◽  
pp. 37-54 ◽  
Author(s):  
Ieda M. Santos
Keyword(s):  
Higher Education ◽  
Mobile Devices ◽  
Cultural Change ◽  
Teaching And Learning ◽  
Bring Your Own Device ◽  
Program Outcomes ◽  
University Campuses ◽  
Working Together ◽  
Personal Mobile Devices ◽  
Joint Enterprise

More and more students are bringing personal mobile devices such as smart phones and iPads to university campuses. Widespread mobile device ownership among students offers Higher Education (HE) institutions with opportunities to explore those devices to support teaching and learning practices. The idea of using students' personal devices is referred to as “Bring Your Own Device,” or BYOD. This chapter examines opportunities and key challenges often discussed in the literature and associated with a BYOD program. Outcomes suggest that a cultural change is necessary to effectively accommodate BYOD in the classroom. The chapter proposes a BYOD joint enterprise consisting of main stakeholders—administrators, faculty, students, and information technology personnel—working together to help minimize the impact of key challenges while maximizing the opportunities afforded by students' everyday mobile devices.

scholarly journals Design of facial recognition system implemented in an unmanned aerial vehicle for citizen security in Latin America

2019 ◽  
Vol 27 ◽  
pp. 04002
Author(s):  
Diego Herrera ◽  
Hiroki Imamura
Keyword(s):  
Neural Network ◽  
Latin America ◽  
Deep Learning ◽  
Mobile Devices ◽  
Unmanned Aerial Vehicle ◽  
Facial Recognition ◽  
Recognition System ◽  
Criminal Records ◽  
Aerial Vehicle ◽  
Facial Recognition System

In the new technological era, facial recognition has become a central issue for a great number of engineers. Currently, there are a great number of techniques for facial recognition, but in this research, we focus on the use of deep learning. The problems with current facial recognition convection systems are that they are developed in non-mobile devices. This research intends to develop a Facial Recognition System implemented in an unmanned aerial vehicle of the quadcopter type. While it is true, there are quadcopters capable of detecting faces and/or shapes and following them, but most are for fun and entertainment. This research focuses on the facial recognition of people with criminal records, for which a neural network is trained. The Caffe framework is used for the training of a convolutional neural network. The system is developed on the NVIDIA Jetson TX2 motherboard. The design and construction of the quadcopter are done from scratch because we need the UAV for adapt to our requirements. This research aims to reduce violence and crime in Latin America.

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