A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms

S Xiong, H Zhang - Journal of Computer Science …, 2024 - journals.bilpubgroup.com
In the digital age, the widespread use of Android devices has led to a surge in security
threats, especially malware. Android, as the most popular mobile operating system, is a …

[HTML][HTML] Vulnerability to cyberattacks and sociotechnical solutions for health care systems: systematic review

P Ewoh, T Vartiainen - Journal of medical internet research, 2024 - jmir.org
Background Health care organizations worldwide are faced with an increasing number of
cyberattacks and threats to their critical infrastructure. These cyberattacks cause significant …

Formulating global policies and strategies for combating criminal use and abuse of artificial intelligence

AD Samuel-Okon, O Olateju, SU Okon… - Available at SSRN …, 2024 - papers.ssrn.com
This study investigates the criminal use and abuse of artificial intelligence (AI), exploring the
effectiveness of various mitigation strategies. It employs a mixed-methods approach …

The revolution and vision of explainable AI for android malware detection and protection

S Ullah, J Li, F Ullah, J Chen, I Ali, S Khan, A Ahad… - Internet of Things, 2024 - Elsevier
The rise and exponential growth in complexity and widespread use of Android mobile
devices have resulted in corresponding detrimental consequences within the realm of cyber …

Real-time system call-based ransomware detection

CJW Chew, V Kumar, P Patros, R Malik - International Journal of …, 2024 - Springer
Ransomware, particularly crypto ransomware, has emerged as the go-to malware for threat
actors aiming to compromise data on Android devices as well as in general. In this paper …

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

A Mahindru, H Arora, A Kumar, SK Gupta… - Scientific Reports, 2024 - nature.com
The challenge of developing an Android malware detection framework that can identify
malware in real-world apps is difficult for academicians and researchers. The vulnerability …

{DARKFLEECE}: Probing the Dark Side of Android Subscription Apps

C Yue, C Zhong, K Chen, Z Zhang, Y Lee - 33rd USENIX Security …, 2024 - usenix.org
Fleeceware, a novel category of malicious subscription apps, is increasingly tricking users
into expensive subscriptions, leading to substantial financial consequences. These apps' …

Detecting Overlay Attacks in Android

A Kar, N Stakhanova, E Branca - Procedia Computer Science, 2024 - Elsevier
Overlay attacks have long been a significant security concern affecting Android devices.
Despite Android touch prevention mechanisms for external apps, internal apps and those …

Android malware detection through centrality analysis of applications network

A Mafakheri, S Sulaimany - Applied Soft Computing, 2024 - Elsevier
Android OS is a widely-used platform for mobile devices. However, with the increasing
number of Android applications and ongoing advancements in application development …

Unraveling the Key of Machine Learning Solutions for Android Malware Detection

J Liu, J Zeng, F Pierazzi, L Cavallaro… - arXiv preprint arXiv …, 2024 - arxiv.org
Android malware detection serves as the front line against malicious apps. With the rapid
advancement of machine learning (ML), ML-based Android malware detection has attracted …