A review of android malware detection approaches based on machine learning

K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are develo** rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

The evolution of android malware and android analysis techniques

K Tam, A Feizollah, NB Anuar, R Salleh… - ACM Computing …, 2017 - dl.acm.org
With the integration of mobile devices into daily life, smartphones are privy to increasing
amounts of sensitive information. Sophisticated mobile malware, particularly Android …

Cryptolock (and drop it): stop** ransomware attacks on user data

N Scaife, H Carter, P Traynor… - 2016 IEEE 36th …, 2016 - ieeexplore.ieee.org
Ransomware is a growing threat that encrypts auser's files and holds the decryption key until
a ransom ispaid by the victim. This type of malware is responsible fortens of millions of …

[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 …

Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version)

L Onwuzurike, E Mariconti, P Andriotis… - ACM Transactions on …, 2019 - dl.acm.org
As Android has become increasingly popular, so has malware targeting it, thus motivating
the research community to propose different detection techniques. However, the constant …

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 …

Mamadroid: Detecting android malware by building markov chains of behavioral models

E Mariconti, L Onwuzurike, P Andriotis… - arxiv preprint arxiv …, 2016 - arxiv.org
The rise in popularity of the Android platform has resulted in an explosion of malware threats
targeting it. As both Android malware and the operating system itself constantly evolve, it is …

Mobile malware attacks: Review, taxonomy & future directions

A Qamar, A Karim, V Chang - Future Generation Computer Systems, 2019 - Elsevier
A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent
years. Android's popular and attractive environment not only captured the attention of users …

A {Large-scale} analysis of the security of embedded firmwares

A Costin, J Zaddach, A Francillon… - 23rd USENIX security …, 2014 - usenix.org
As embedded systems are more than ever present in our society, their security is becoming
an increasingly important issue. However, based on the results of many recent analyses of …

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 …