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[HTML][HTML] An in-depth review of machine learning based Android malware detection
It is estimated that around 70% of mobile phone users have an Android device. Due to this
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset
Cybersecurity has become a major concern for society, mainly motivated by the increasing
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
Static malware analysis using low-parameter machine learning models
R Baker del Aguila, CD Contreras Pérez… - Computers, 2024 - mdpi.com
Recent advancements in cybersecurity threats and malware have brought into question the
safety of modern software and computer systems. As a direct result of this, artificial …
safety of modern software and computer systems. As a direct result of this, artificial …
Visualising static features and classifying android malware using a convolutional neural network approach
Android phones are widely recognised as the most popular mobile phone operating system.
Additionally, tasks like browsing the internet, taking pictures, making calls, and sending …
Additionally, tasks like browsing the internet, taking pictures, making calls, and sending …
Innovative approach to android malware detection: prioritizing critical features using rough set theory
The widespread integration of smartphones into modern society has profoundly impacted
various aspects of our lives, revolutionizing communication, work, entertainment, and access …
various aspects of our lives, revolutionizing communication, work, entertainment, and access …
A Survey on Android Malware Detection Techniques Using Supervised Machine Learning
S Altaha, A Aljughiman, S Gul - IEEE Access, 2024 - ieeexplore.ieee.org
Android's open-source nature has contributed to the platform's rapid growth and its
widespread adoption. However, this widespread adoption of the Android operating system …
widespread adoption. However, this widespread adoption of the Android operating system …
A new method for tuning the CNN pre-trained models as a feature extractor for malware detection
H Bakır - Pattern Analysis and Applications, 2025 - Springer
Despite significant advancements in Android malware detection, current approaches face
notable challenges, particularly in handling obfuscation techniques, achieving high …
notable challenges, particularly in handling obfuscation techniques, achieving high …
The Effect of the Ransomware Dataset Age on the Detection Accuracy of Machine Learning Models
QM Yaseen - Information, 2023 - mdpi.com
Several supervised machine learning models have been proposed and used to detect
Android ransomware. These models were trained using different datasets from different …
Android ransomware. These models were trained using different datasets from different …
Firmwaredroid: Towards automated static analysis of pre-installed android apps
T Sutter, B Tellenbach - 2023 IEEE/ACM 10th International …, 2023 - ieeexplore.ieee.org
Supply chain attacks are an evolving threat to the IoT and mobile landscape. Recent
malware findings have shown that even sizeable mobile phone vendors cannot defend their …
malware findings have shown that even sizeable mobile phone vendors cannot defend their …
Droiddissector: A static and dynamic analysis tool for android malware detection
DroidDissector is an extraction tool for both static and dynamic features. The aim is to
provide Android malware researchers and analysts with an integrated tool that can extract …
provide Android malware researchers and analysts with an integrated tool that can extract …