A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms

P Maniriho, AN Mahmood, MJM Chowdhury - ACM Computing Surveys, 2024 - dl.acm.org
Malware is one of the most common and severe cyber threats today. Malware infects
millions of devices and can perform several malicious activities including compromising …

DeepCatra: Learning flow‐and graph‐based behaviours for Android malware detection

Y Wu, J Shi, P Wang, D Zeng, C Sun - IET Information Security, 2023 - Wiley Online Library
As Android malware grows and evolves, deep learning has been introduced into malware
detection, resulting in great effectiveness. Recent work is considering hybrid models and …

GHGDroid: Global heterogeneous graph-based android malware detection

L Shen, M Fang, J Xu - Computers & Security, 2024 - Elsevier
As the most popular mobile platform, Android has become the major attack target of
malware, and thus there is an urgent need to effectively thwart them. Recently, the graph …

EAODroid: Android Malware Detection Based on Enhanced API Order

L Huang, J Xue, Y Wang, D Qu, J Chen… - Chinese Journal of …, 2023 - ieeexplore.ieee.org
The development of smart mobile devices brings convenience to people's lives, but also
provides a breeding ground for Android malware. The sharp increasing malware poses a …

[HTML][HTML] Enhancing android malware detection explainability through function call graph APIs

D Soi, A Sanna, D Maiorca, G Giacinto - Journal of Information Security and …, 2024 - Elsevier
Nowadays, mobile devices are massively used in everyday activities. Thus, they contain
sensitive data targeted by threat actors like bank accounts and personal information …

Use of Graph Neural Networks in Aiding Defensive Cyber Operations

S Mitra, T Chakraborty, S Neupane, A Piplai… - arXiv preprint arXiv …, 2024 - arxiv.org
In an increasingly interconnected world, where information is the lifeblood of modern
society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital …

DFRMIdroid: A Comprehensive Fusion Approach Utilizing Permissions and Intents Analysis with the DFR-MI Algorithm for Enhanced Malware Detection on Android …

IM Ibrahim, AB Sallow - Revue d'Intelligence Artificielle, 2024 - search.ebscohost.com
Smartphones based on the Android operating system are increasingly popular due to their
multifunctional capabilities in various fields. However, these functions have also encouraged …

MalGA-LSTM: a malicious code detection model based on genetic algorithm optimising LSTM trainable parameters

Y Zhang, Y Feng, Y Zhao - International Journal of Security …, 2023 - inderscienceonline.com
With the development of internet technology, the number of malicious software is also
growing rapidly, causing great potential for cybersecurity issues. When using neural network …

Mobile based Malware Detection using Artificial Intelligence Techniques a review

SS Jasim - Journal of Al-Qadisiyah for Computer Science and …, 2024 - jqcsm.qu.edu.iq
Malware attacks on mobile devices are becoming more common and more complicated
every year. Malware writers see the open-source Android app as their main target because …

An Android Malware Detection Method Based on Optimized Feature Extraction Using Graph Convolutional Network

Z Wang, Z Wang, Y Zhang - … Conference on Digital Forensics and Cyber …, 2023 - Springer
With the development of the mobile Internet, mobile devices have been extensively
promoted and popularized. Android, as the current popular mobile intelligent operating …