Results 51 to 60 of about 20,046 (244)

Monitoring Real Android Malware [PDF]

, 2015
In the most comprehensive study on Android attacks so far (undertaken by the Android Malware Genome Project), the behaviour of more than 1, 200 malwares was analysed and categorised into common, recurring groups of attacks. Based on this work (and the corresponding actual malware files), we present an approach for specifying and identifying these (and ...
Jan-Christoph Küster, Andreas Bauer
openaire   +1 more source

Android Malware Family Classification Based on Resource Consumption over Time

, 2017
The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years.
Aniello, Leonardo, Baldoni, Roberto, Ciccotelli, Claudio, Massarelli, Luca, Querzoni, Leonardo, Ucci, Daniele   +5 more
core   +1 more source

Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned [PDF]

, 2018
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 families active on the platform.
Stringhini, Gianluca, Suarez-Tangil, Guillermo   +1 more
core  

Forensic Analysis of Hook Android Malware

Forensic Science International: Digital Investigation, 2023
This publication presents a thorough forensic investigation of the banking malware known as Hook, shedding light on its intricate functionalities and providing valuable insights into the broader realm of banking malware. Given the persistent evolution of Android malware, particularly in the context of banking threats, this research explores the ongoing
Dominic Schmutz, Robin Rapp, Benjamin Fehrensen   +2 more
openaire   +1 more source

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +3 more
wiley   +1 more source

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

IEEE Access
Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets.
Mohammed Maray, Mashael Maashi, Haya Mesfer Alshahrani, Sumayh S. Aljameel, Sitelbanat Abdelbagi, Ahmed S. Salama   +5 more
doaj   +1 more source

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection [PDF]

Vietnam Journal of Computer Science, 2020
Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on
Md. Shohel Rana, Andrew H. Sung
doaj   +1 more source

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

, 2018
Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary.
Chen, Xiao, Li, Chaoran, Nepal, Surya, Ren, Kui, Wang, Derui, Wen, Sheng, Xiang, Yang, Zhang, Jun   +7 more
core   +1 more source

Explaining Black-box Android Malware Detection [PDF]

2018 26th European Signal Processing Conference (EUSIPCO), 2018
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions.
Marco Melis, Davide Maiorca, Battista Biggio, Giorgio Giacinto, Fabio Roli   +4 more
openaire   +4 more sources

Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

Engineering Reports, Volume 8, Issue 1, January 2026.
Overview of the proposed RF–ViT ensemble for multi‐class malware classification. Textual (BoW/byte‐frequency) and visual representations are combined via a product rule, achieving improved accuracy and robustness over individual models. ABSTRACT Malware remains a significant concern for modern digital systems, increasing the need for reliable and ...
Fadi Makarem, Rida El Chall, Abed Ellatif Samhat, Khouloud Samrouth, Nader Bakir   +4 more
wiley   +1 more source