On-Device Mobile Phone Security Exploits Machine Learning

2017 ◽  
Vol 16 (2) ◽  
pp. 92-96 ◽  
Author(s):  
Nayeem Islam ◽  
Saumitra Das ◽  
Yin Chen
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Mobile Phone Security

scholarly journals Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions

The Analyst ◽  
2018 ◽  
Vol 143 (9) ◽  
pp. 2066-2075 ◽  
Author(s):  
Y. Rong ◽  
A. V. Padron ◽  
K. J. Hagerty ◽  
N. Nelson ◽  
S. Chi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Impedimetric Biosensor ◽  
Post Hoc

We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.

Open source column: Deep learning in the browser

2019 ◽  
Vol 11 (1) ◽  
pp. 1-1
Author(s):  
Sabrina Kletz ◽  
Marco Bertini ◽  
Mathias Lux
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Phone ◽  
Open Source ◽  
Web Pages ◽  
Web Browsers ◽  
Open Source Framework ◽  
Interesting Approach ◽  
Cross Platform ◽  
Client Device

Having already discussed MatConvNet and Keras, let us continue with an open source framework for deep learning, which takes a new and interesting approach. TensorFlow.js is not only providing deep learning for JavaScript developers, but it's also making applications of deep learning available in the WebGL enabled web browsers, or more specifically, Chrome, Chromium-based browsers, Safari and Firefox. Recently node.js support has been added, so TensorFlow.js can be used to directly control TensorFlow without the browser. TensorFlow.js is easy to install. As soon as a browser is installed one is ready to go. Browser based, cross platform applications, e.g. running with Electron, can also make use of TensorFlow.js without an additional install. The performance, however, depends on the browser the client is running, and memory and GPU on the client device. More specifically, one cannot expect to analyze 4K videos on a mobile phone in real time. While it's easy to install, and it's easy to develop based on TensorFlow.js, there are drawbacks: (i) developers have less control over where the machine learning actually takes place (e.g. on CPU or GPU), that it is running in the same sandbox as all web pages in the browser do, and (ii) that in the current release it still has rough edges and is not considered stable enough to use in production.

Threats and Vulnerabilities of Mobile Applications

2021 ◽  
pp. 473-492
Author(s):  
Thangavel M. ◽  
Divyaprabha M. ◽  
Abinaya C.
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Mobile Device ◽  
Mobile Applications ◽  
Smart Devices ◽  
Wireless Multimedia ◽  
Security Issues ◽  
Major Threat ◽  
Complete Set ◽  
Mobile Phone Security

Smart devices like mobile phones, tablets, and laptops have become necessities in our lives due to the services they provide. However, in recent days, mobile applications have become a major threat for an attack. One of the most attractive features of smartphones is the availability of a large number of apps for users to download and install. However, it also means hackers can easily distribute malware to smartphones, launching various attacks. Each day, a mobile device attack is changing dynamically, and it is very difficult to represent a complete set of threats and vulnerabilities. Mobile phone security has become an important aspect of security issues in wireless multimedia communications. The development of mobile applications has increased drastically; hence, it is our responsibility to protect our devices and the data within them. Being aware is the first step to protect data. Thus, to prevent the mobile from the threats, efforts are required to form the application developer, app market administrator, and user to defend against the malware. This article explores those threats and vulnerabilities of mobile applications.

scholarly journals Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine

2020 ◽  
Vol 41 (7) ◽  
pp. 826-830 ◽  
Author(s):  
Arni S. R. Srinivasa Rao ◽  
Jose A. Vazquez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Web Survey ◽  
Machine Learning Algorithm ◽  
Case Identification ◽  
Susceptible Populations

AbstractWe propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone–based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

Automated Detection and Enumeration of Waterborne Pathogens Using Mobile Phone Microscopy and Machine Learning

2017 ◽  
Author(s):  
Hatice Ceylan Koydemir ◽  
Steve Feng ◽  
Kyle Liang ◽  
Rohan Nadkarni ◽  
Parul Benien ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Automated Detection ◽  
Waterborne Pathogens
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