A systematic literature review of android malware detection using static analysis

Y Pan, X Ge, C Fang, Y Fan - IEEE Access, 2020 - ieeexplore.ieee.org
Android malware has been in an increasing trend in recent years due to the pervasiveness
of Android operating system. Android malware is installed and run on the smartphones …

[HTML][HTML] A survey and evaluation of android-based malware evasion techniques and detection frameworks

P Faruki, R Bhan, V Jain, S Bhatia, N El Madhoun… - Information, 2023 - mdpi.com
Android platform security is an active area of research where malware detection techniques
continuously evolve to identify novel malware and improve the timely and accurate detection …

Deep reinforcement adversarial learning against botnet evasion attacks

G Apruzzese, M Andreolini, M Marchetti… - … on Network and …, 2020 - ieeexplore.ieee.org
As cybersecurity detectors increasingly rely on machine learning mechanisms, attacks to
these defenses escalate as well. Supervised classifiers are prone to adversarial evasion …

SEDMDroid: An enhanced stacking ensemble framework for Android malware detection

H Zhu, Y Li, R Li, J Li, Z You… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The popularity of the Android platform in smartphones and other Internet-of-Things devices
has resulted in the explosive of malware attacks against it. Malware presents a serious …

Android malware detection as a bi-level problem

M Jerbi, ZC Dagdia, S Bechikh, LB Said - Computers & Security, 2022 - Elsevier
Malware detection is still a very challenging topic in the cybersecurity field. This is mainly
due to the use of obfuscation techniques. To solve this issue, researchers proposed to …

RiskCog: Unobtrusive real-time user authentication on mobile devices in the wild

T Zhu, Z Qu, H Xu, J Zhang, Z Shao… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Recent hardware advances have led to the development and consumerization of mobile
devices, which mainly include smartphones and various wearable devices. To protect the …

Semantics-preserving reinforcement learning attack against graph neural networks for malware detection

L Zhang, P Liu, YH Choi, P Chen - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As an increasing number of deep-learning-based malware scanners have been proposed,
the existing evasion techniques, including code obfuscation and polymorphic malware, are …

A hybrid deep network framework for android malware detection

HJ Zhu, LM Wang, S Zhong, Y Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Android is a growing target for malicious software (malware) because of its popularity and
functionality. Malware poses a serious threat to users' privacy, money, equipment and file …

A dynamic DL-driven architecture to combat sophisticated Android malware

I Bibi, A Akhunzada, J Malik, J Iqbal, A Musaddiq… - IEEE …, 2020 - ieeexplore.ieee.org
The predominant Android operating system has captured enormous attention globally not
only in smart phone industry but also for varied smart devices. The open architecture and …

[HTML][HTML] A review on android malware: Attacks, countermeasures and challenges ahead

SG Selvaganapathy… - Journal of Cyber …, 2021 - journals.riverpublishers.com
Smartphones usage have become ubiquitous in modern life serving as a double-edged
sword with opportunities and challenges in it. Along with the benefits, smartphones also …