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 …

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software.

L Yuanming, R Latih - International Journal on Advanced …, 2024 - search.ebscohost.com
With the continuous development of technology, the types of malware and their variants
continue to increase, which has become an enormous challenge to network security. These …

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 …

[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 …

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 …

A Review of Deep Learning Based Malware Detection Techniques

H Wang, B Cui, Q Yuan, R Shi, M Huang - Neurocomputing, 2024 - Elsevier
With the popularization of computer technology, the number of malware has increased
dramatically in recent years. Some malware can threaten the network security of users by …

Leveraging application permissions and network traffic attributes for Android ransomware detection

SR Jeremiah, H Chen, S Gritzalis, JH Park - Journal of Network and …, 2024 - Elsevier
The increase in ransomware threats targeting Android devices necessitates the
development of advanced techniques to strengthen the effectiveness of detection and …

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 …

A brief survey of deep learning methods for android Malware detection

A Joomye, MH Ling, KLA Yau - International Journal of System Assurance …, 2024 - Springer
As the number of malware attacks continues to grow year by year with increasing
complexity, Android devices have remained vulnerable with over 30 million mobile attacks …