Apposcopy: Semantics-based detection of android malware through static analysis

Y Feng, S Anand, I Dillig, A Aiken - Proceedings of the 22nd ACM …, 2014 - dl.acm.org
We present Apposcopy, a new semantics-based approach for identifying a prevalent class of
Android malware that steals private user information. Apposcopy incorporates (i) a high …

Androdialysis: Analysis of android intent effectiveness in malware detection

A Feizollah, NB Anuar, R Salleh, G Suarez-Tangil… - computers & …, 2017 - Elsevier
The wide popularity of Android systems has been accompanied by increase in the number
of malware targeting these systems. This is largely due to the open nature of the Android …

Exploring permission-induced risk in android applications for malicious application detection

W Wang, X Wang, D Feng, J Liu… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
Android has been a major target of malicious applications (malapps). How to detect and
keep the malapps out of the app markets is an ongoing challenge. One of the central design …

[HTML][HTML] Kronodroid: time-based hybrid-featured dataset for effective android malware detection and characterization

A Guerra-Manzanares, H Bahsi, S Nõmm - Computers & Security, 2021 - Elsevier
Android malware evolution has been neglected by the available data sets, thus providing a
static snapshot of a non-stationary phenomenon. The impact of the time variable has not had …

{WHYPER}: Towards automating risk assessment of mobile applications

R Pandita, X Xiao, W Yang, W Enck, T Xie - 22nd USENIX Security …, 2013 - usenix.org
Application markets such as Apple's App Store and Google's Play Store have played an
important role in the popularity of smartphones and mobile devices. However, keeping …

PIndroid: A novel Android malware detection system using ensemble learning methods

F Idrees, M Rajarajan, M Conti, TM Chen… - Computers & …, 2017 - Elsevier
The extensive use of smartphones has been a major driving force behind a drastic increase
of malware attacks. Covert techniques used by the malware make them hard to detect with …

Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers

W Wang, Y Li, X Wang, J Liu, X Zhang - Future generation computer …, 2018 - Elsevier
Android platform has dominated the markets of smart mobile devices in recent years. The
number of Android applications (apps) has seen a massive surge. Unsurprisingly, Android …

[PDF][PDF] A machine-learning approach for classifying and categorizing android sources and sinks.

S Rasthofer, S Arzt, E Bodden - NDSS, 2014 - bodden.de
Today's smartphone users face a security dilemma: many apps they install operate on
privacy-sensitive data, although they might originate from developers whose trustworthiness …

Lightweight, obfuscation-resilient detection and family identification of android malware

J Garcia, M Hammad, S Malek - ACM Transactions on Software …, 2018 - dl.acm.org
The number of malicious Android apps is increasing rapidly. Android malware can damage
or alter other files or settings, install additional applications, and so on. To determine such …

Droidchameleon: evaluating android anti-malware against transformation attacks

V Rastogi, Y Chen, X Jiang - Proceedings of the 8th ACM SIGSAC …, 2013 - dl.acm.org
Mobile malware threats have recently become a real concern. In this paper, we evaluate the
state-of-the-art commercial mobile antimalware products for Android and test how resistant …