[HTML][HTML] Machine learning for android malware detection: mission accomplished? a comprehensive review of open challenges and future perspectives

A Guerra-Manzanares - Computers & Security, 2024 - Elsevier
The extensive research in machine learning based Android malware detection showcases
high-performance metrics through a wide range of proposed solutions. Consequently, this …

Automated android malware detection using optimal ensemble learning approach for cybersecurity

H Alamro, W Mtouaa, S Aljameel, AS Salama… - IEEE …, 2023 - ieeexplore.ieee.org
Current technological advancement in computer systems has transformed the lives of
humans from real to virtual environments. Malware is unnecessary software that is often …

A novel neural network architecture using automated correlated feature layer to detect android malware applications

A Alabrah - Mathematics, 2023 - mdpi.com
Android OS devices are the most widely used mobile devices globally. The open-source
nature and less restricted nature of the Android application store welcome malicious apps …

Identifying the Mutual Correlations and Evaluating the Weights of Factors and Consequences of Mobile Application Insecurity

E Zaitseva, T Hovorushchenko, O Pavlova, Y Voichur - Systems, 2023 - mdpi.com
Currently, there is a contradiction between the growing number of mobile applications in use
and the responsibility that is placed on them, on the one hand, and the imperfection of the …

Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN

KA Dhanya, P Vinod, SY Yerima, A Bashar… - IEEE Systems …, 2023 - ieeexplore.ieee.org
The threat of malware in the Internet of Things (IoT) is ever-present given that many IoT
systems today rely on the Android operating system. There has been a consistent rise in …

Experts still needed: boosting long-term android malware detection with active learning

A Guerra-Manzanares, H Bahsi - Journal of Computer Virology and …, 2024 - Springer
The continuous evolution of cyber threats imposes a critical challenge to malware detection
systems, so operational detection solutions in real-world settings must keep up-to-date …

Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

M Chaieb, MA Ghorab, MA Saied - arXiv preprint arXiv:2405.03620, 2024 - arxiv.org
As cyber threats and malware attacks increasingly alarm both individuals and businesses,
the urgency for proactive malware countermeasures intensifies. This has driven a rising …

An efficient security testing for android application based on behavior and activities using RFE-MLP and ensemble classifier

P Kumar, S Singh - Multimedia Tools and Applications, 2024 - Springer
An enormous amount of applications that are available for download permits users to
enhance the functionality of the devices with brand-new features, which is a significant factor …

Android malware detection using time-aware machine learning approach

AMR AlSobeh, K Gaber, MM Hammad, M Nuser… - Cluster …, 2024 - Springer
In today's rapidly evolving digital landscape, the surge in smartphone usage is paralleled by
an increasing wave of cyberthreats, highlighting the limitations of existing signature-based …

On the application of active learning to handle data evolution in Android malware detection

A Guerra-Manzanares, H Bahsi - … on Digital Forensics and Cyber Crime, 2022 - Springer
Mobile malware detection remains a significant challenge in the rapidly evolving cyber
threat landscape. Although the research about the application of machine learning methods …