A survey of app store analysis for software engineering

W Martin, F Sarro, Y Jia, Y Zhang… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
App Store Analysis studies information about applications obtained from app stores. App
stores provide a wealth of information derived from users that would not exist had the …

Android security: a survey of issues, malware penetration, and defenses

P Faruki, A Bharmal, V Laxmi… - … surveys & tutorials, 2014 - ieeexplore.ieee.org
Smartphones have become pervasive due to the availability of office applications, Internet,
games, vehicle guidance using location-based services apart from conventional services …

MLDroid—framework for Android malware detection using machine learning techniques

A Mahindru, AL Sangal - Neural Computing and Applications, 2021 - Springer
This research paper presents MLDroid—a web-based framework—which helps to detect
malware from Android devices. Due to increase in the popularity of Android devices …

[PDF][PDF] Drebin: Effective and explainable detection of android malware in your pocket.

D Arp, M Spreitzenbarth, M Hubner, H Gascon… - Ndss, 2014 - media.telefonicatech.com
Malicious applications pose a threat to the security of the Android platform. The growing
amount and diversity of these applications render conventional defenses largely ineffective …

Droiddetector: android malware characterization and detection using deep learning

Z Yuan, Y Lu, Y Xue - Tsinghua Science and Technology, 2016 - ieeexplore.ieee.org
Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android
has been the most popular mobile operating system since 2012. However, owing to the …

Sok: Security evaluation of home-based iot deployments

O Alrawi, C Lever, M Antonakakis… - 2019 IEEE symposium …, 2019 - ieeexplore.ieee.org
Home-based IoT devices have a bleak reputation regarding their security practices. On the
surface, the insecurities of IoT devices seem to be caused by integration problems that may …

Yes, machine learning can be more secure! a case study on android malware detection

A Demontis, M Melis, B Biggio… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
To cope with the increasing variability and sophistication of modern attacks, machine
learning has been widely adopted as a statistically-sound tool for malware detection …

The dark side of perceptual manipulations in virtual reality

WJ Tseng, E Bonnail, M McGill, M Khamis… - Proceedings of the …, 2022 - dl.acm.org
“Virtual-Physical Perceptual Manipulations”(VPPMs) such as redirected walking and haptics
expand the user's capacity to interact with Virtual Reality (VR) beyond what would ordinarily …

Privacy concerns for mobile app download: An elaboration likelihood model perspective

J Gu, YC Xu, H Xu, C Zhang, H Ling - Decision Support Systems, 2017 - Elsevier
In the mobile age, protecting users' information from privacy-invasive apps becomes
increasingly critical. To precaution users against possible privacy risks, a few Android app …

Pscout: analyzing the android permission specification

KWY Au, YF Zhou, Z Huang, D Lie - … of the 2012 ACM conference on …, 2012 - dl.acm.org
Modern smartphone operating systems (OSs) have been developed with a greater
emphasis on security and protecting privacy. One of the mechanisms these systems use to …