GDroid: Android malware detection and classification with graph convolutional network

H Gao, S Cheng, W Zhang - Computers & Security, 2021 - Elsevier
The dramatic increase in the number of malware poses a serious challenge to the Android
platform and makes it difficult for malware analysis. In this paper, we propose a novel …

Android malware family classification and analysis: Current status and future directions

F Alswaina, K Elleithy - Electronics, 2020 - mdpi.com
Android receives major attention from security practitioners and researchers due to the influx
number of malicious applications. For the past twelve years, Android malicious applications …

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

R Jusoh, A Firdaus, S Anwar, MZ Osman… - PeerJ Computer …, 2021 - peerj.com
Android is a free open-source operating system (OS), which allows an in-depth
understanding of its architecture. Therefore, many manufacturers are utilizing this OS to …

Didroid: Android malware classification and characterization using deep image learning

A Rahali, AH Lashkari, G Kaur, L Taheri… - Proceedings of the …, 2020 - dl.acm.org
The unrivaled threat of android malware is the root cause of various security problems on
the internet. Although there are remarkable efforts in detection and classification of android …

Less is More: A privacy-respecting Android malware classifier using federated learning

R Gálvez, V Moonsamy, C Diaz - arXiv preprint arXiv:2007.08319, 2020 - arxiv.org
In this paper we present LiM (" Less is More"), a malware classification framework that
leverages Federated Learning to detect and classify malicious apps in a privacy-respecting …

GCDroid: android malware detection based on graph compression with reachability relationship extraction for IoT devices

W Niu, Y Wang, X Liu, R Yan, X Li… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
With the widespread popularity of Internet of Things (IoT) devices based on the Android
system, the amount of Android malware targeting IoT devices continues to increase, causing …

WHGDroid: Effective android malware detection based on weighted heterogeneous graph

L Huang, J Xue, Y Wang, Z Liu, J Chen… - Journal of Information …, 2023 - Elsevier
The growing Android malware is seriously threatening the privacy and property security of
Android users. However, the existing detection methods are often unable to maintain …

Eight years of rider measurement in the android malware ecosystem: evolution and lessons learned

G Suarez-Tangil, G Stringhini - arXiv preprint arXiv:1801.08115, 2018 - arxiv.org
Despite the growing threat posed by Android malware, the research community is still
lacking a comprehensive view of common behaviors and trends exposed by malware …

Malware-on-the-brain: Illuminating malware byte codes with images for malware classification

F Zhong, Z Chen, M Xu, G Zhang, D Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Malware is a piece of software that was written with the intent of doing harm to data, devices,
or people. Since a number of new malware variants can be generated by reusing codes …

Eight years of rider measurement in the android malware ecosystem

G Suarez-Tangil, G Stringhini - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Despite the growing threat posed by the Android malware, the research community is still
lacking a comprehensive view of common behaviors and emerging trends in malware …