Android Malware Detection Based on Factorization Machine
IEEE Access, 2019 As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security.
Chenglin Li +5 more
openaire +3 more sources
Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis
InformationMobile 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
Android malware detection with MH-100K: An innovative dataset for advanced research. [PDF]
Data Brief, 2023 Bragança H +5 more
europepmc +1 more source
MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection
IET SoftwareNowadays, 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
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
A Resilient Deep Learning Framework for Mobile Malware Detection: From Architecture to Deployment
Future InternetMobile 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 +2 more
doaj +1 more source
Android malware detection method based on highly distinguishable static features and DenseNet. [PDF]
PLoS One, 2022 Yang J, Zhang Z, Zhang H, Fan J.
europepmc +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
Hybrid Android Malware Detection: A Review of Heuristic-Based Approach
IEEE AccessOver the last decade, numerous research efforts have been dedicated to countering malicious mobile applications. Given its market share, Android OS has been the primary target for most of these apps. Researchers have devised numerous solutions to protect Android devices and their users, categorizing them into static and dynamic approaches.
Rajif Agung Yunmar +3 more
openaire +2 more sources
FG-Droid: Grouping based feature size reduction for Android malware detection. [PDF]
PeerJ Comput Sci, 2022 Arslan RS.
europepmc +1 more source