Results 91 to 100 of about 3,123 (222)

Android Malware Detection using ML

International Journal of Advanced Research in Science, Communication and Technology
Android devices are more prone of malware attacks due to its open-source nature. This makes it easier for installing applications from various sources, which can lead to major security issues. Machine learning learns from examples. It studies data from apps both good and bad and understands its characteristics.
null Pradhipa S, null Poornima, null Indu A, null Reena K   +3 more
openaire   +1 more source

AAGAN: Android Malware Generation System Based on Generative Adversarial Network

Vietnam Journal of Computer Science
With the rapid evolution of mobile malware, especially Android malware, machine learning (ML)-based Android malware detection systems have drawn massive attention. Although ML algorithms have recently led to many vital breakthroughs in malware detection,
Doan Minh Trung, Nghi Hoang Khoa, Phan The Duy, Van-Hau Pham, Nguyen Tan Cam   +4 more
doaj   +1 more source

Enhancing malware detection in Android application by incorporating broadcast receivers

, 2021
Rachael Havens, Halil Bişğin, Vincent C. Nwobodo, Fadi Mohsen   +3 more
openalex   +2 more sources

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, Keith Mills, Di Niu, Rui Zhu, Hongwen Zhang, Husam Kinawi   +5 more
openaire   +3 more sources

PICAndro: Packet InspeCtion-Based Android Malware Detection [PDF]

, 2021
Vikas Sihag, Gaurav Choudhary, Manu Vardhan, Pradeep Kumar Singh, Jung Taek Seo   +4 more
openalex   +1 more source

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

Using Dalvik opcodes for malware detection on android [PDF]

, 2017
José Gaviria de la Puerta, Borja Sanz
openalex   +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

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

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