Results 101 to 110 of about 3,123 (222)

A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features [PDF]

, 2019
Rajesh Kumar, Xiaosong Zhang, Wenyong Wang, Riaz Ullah Khan, Jay Kumar, Abubakar Sharif   +5 more
openalex   +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 Access
Over 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, Sri Suning Kusumawardani, null Widyawan, Fadi Mohsen   +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

CFSBFDroid: Android Malware Detection Using CFS + Best First Search-Based Feature Selection [PDF]

, 2022
Ravi Mohan Sharma, Chaitanya P. Agrawal, Vinod Kumar, Adugna Necho Mulatu   +3 more
openalex   +1 more source

Optimal Malware Detection for Android

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Abstract—The increasing prevalence of Android devices has led to a surge in malicious apps targeting the platform. We present an Automated Android Malware Detection system using an Optimal Ensemble Learning Approach to combat this. This system integrates machine learning algorithms like Random Forest, Gradient Boosting, and Convolutional Neural ...
Tilak Suresh, Deepika M S, Dr.Anandaraj SP, Dr.Poornima S   +3 more
openaire   +1 more source

DL-AMDet: Deep learning-based malware detector for android

Intelligent Systems with Applications
The Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of
Ahmed R. Nasser, Ahmed M. Hasan, Amjad J. Humaidi   +2 more
doaj   +1 more source

Earthworm optimization algorithm based cascade LSTM-GRU model for android malware detection

Cyber Security and Applications
The rise in mobile malware risks brought on by the explosion of Android smartphones required more efficient detection techniques. Inspired by a cascade of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, optimized using the ...
Brij B. Gupta, Akshat Gaurav, Varsha Arya, Shavi Bansal, Razaz Waheeb Attar, Ahmed Alhomoud, Konstantinos Psannis   +6 more
doaj   +1 more source

Active Learning Model Used for Android Malware Detection

, 2023
Md Yeasin
openalex   +1 more source

Hybrid Android Malware Detection and Classification Using Deep Neural Networks [PDF]

Muhammad Rashid, Shahnawaz Qureshi, Arslan Abid, Saad Said Alqahtany, Ali Alqazzaz, Mahmood ul Hassan, Mana Saleh Al Reshan, Asadullah Shaikh   +7 more
openalex   +1 more source