[PDF][PDF] Copperdroid: Automatic reconstruction of android malware behaviors.

K Tam, SJ Khan, A Fattori, L Cavallaro - Ndss, 2015 - core.ac.uk
Mobile devices and their application marketplaces drive the entire economy of the today's
mobile landscape. Android platforms alone have produced staggering revenues, exceeding …

[PDF][PDF] A system call-centric analysis and stimulation technique to automatically reconstruct android malware behaviors

A Reina, A Fattori, L Cavallaro - EuroSec, April, 2013 - artificialstudios.org
With more than 500 million of activations reported in Q3 2012, Android mobile devices are
becoming ubiquitous and trends confirm this is unlikely to slow down. App stores, such as …

{DroidScope}: Seamlessly reconstructing the {OS} and dalvik semantic views for dynamic android malware analysis

LK Yan, H Yin - 21st USENIX security symposium (USENIX security 12), 2012 - usenix.org
The prevalence of mobile platforms, the large market share of Android, plus the openness of
the Android Market makes it a hot target for malware attacks. Once a malware sample has …

MEGDroid: A model-driven event generation framework for dynamic android malware analysis

H Hasan, BT Ladani, B Zamani - Information and Software Technology, 2021 - Elsevier
Context The tremendous growth of Android malware in recent years is a strong motivation
for the vast endeavor in detection and analysis of malware apps. A prominent approach for …

[PDF][PDF] Intellidroid: a targeted input generator for the dynamic analysis of android malware.

MY Wong, D Lie - NDSS, 2016 - ndss-symposium.org
While dynamic malware analysis methods generally provide better precision than purely
static methods, they have the key drawback that they can only detect malicious behavior if it …

Droidscribe: Classifying android malware based on runtime behavior

SK Dash, G Suarez-Tangil, S Khan… - 2016 IEEE Security …, 2016 - ieeexplore.ieee.org
The Android ecosystem has witnessed a surge in malware, which not only puts mobile
devices at risk but also increases the burden on malware analysts assessing and …

A study of run-time behavioral evolution of benign versus malicious apps in android

H Cai, X Fu, A Hamou-Lhadj - Information and Software Technology, 2020 - Elsevier
Context The constant evolution of the Android platform and its applications have imposed
significant challenges both to understanding and securing the Android ecosystem. Yet …

Morpheus: automatically generating heuristics to detect android emulators

Y Jing, Z Zhao, GJ Ahn, H Hu - … of the 30th Annual Computer Security …, 2014 - dl.acm.org
Emulator-based dynamic analysis has been widely deployed in Android application stores.
While it has been proven effective in vetting applications on a large scale, it can be detected …

Mobile-sandbox: having a deeper look into android applications

M Spreitzenbarth, F Freiling, F Echtler… - Proceedings of the 28th …, 2013 - dl.acm.org
Smartphones in general and Android in particular are increasingly shifting into the focus of
cybercriminals. For understanding the threat to security and privacy it is important for security …

Machine learning-based dynamic analysis of Android apps with improved code coverage

SY Yerima, MK Alzaylaee, S Sezer - EURASIP Journal on Information …, 2019 - Springer
This paper investigates the impact of code coverage on machine learning-based dynamic
analysis of Android malware. In order to maximize the code coverage, dynamic analysis on …