Results 131 to 140 of about 2,905 (223)

Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis

Information
Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis ...
Nahier Aldhafferi
doaj   +1 more source

An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs. [PDF]

Sensors (Basel), 2023
Wu H, Luktarhan N, Tian G, Song Y.
europepmc   +1 more source

MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection

IET Software
Nowadays, the Android system is widely used in mobile devices. The existence of malware in the Android system has posed serious security risks. Therefore, detecting malware has become a main research focus for Android devices.
Bo Yang, Mengbo Li, Li Li, Huai Liu
doaj   +1 more source

Optimization of Android Malware Detection Based on Permissions Using LightGBM with Hyperparameter Tuning and Chi-Square Feature Selection

Journal of Information and Technology
This study aims to develop an efficient Android malware detection framework using the LightGBM algorithm optimized through Chi-Square feature selection and hyperparameter tuning.
Dede Wintana, Satia Suhada, Gunawan
doaj   +1 more source

MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection. [PDF]

Sensors (Basel), 2022
Wang X, Zhang L, Zhao K, Ding X, Yu M.
europepmc   +1 more source

FG-Droid: Grouping based feature size reduction for Android malware detection. [PDF]

PeerJ Comput Sci, 2022
Arslan RS.
europepmc   +1 more source

Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers. [PDF]

PeerJ Comput Sci, 2022
Atacak İ, Kılıç K, Doğru İA.
europepmc   +1 more source

A Resilient Deep Learning Framework for Mobile Malware Detection: From Architecture to Deployment

Future Internet
Mobile devices are frequent targets of malware due to the large volume of sensitive personal, financial, and corporate data they process. Traditional static, dynamic, and hybrid analysis methods are increasingly insufficient against evolving threats ...
Aysha Alfaw, Mohsen Rouached, Aymen Akremi   +2 more
doaj   +1 more source

HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]

PLoS One
Minh Manh V, Do Xuan C, Van NTK.
europepmc   +1 more source

Reassessing feature-based Android malware detection in a contemporary context. [PDF]

PLoS One
Muzaffar A, Ragab Hassen H, Zantout H, Lones MA.   +3 more
europepmc   +1 more source