An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion

An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion

Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. Consequently, there is a dramatic need for effective Android malware detection methods. This paper suggests a novel fuzzy rank-based fusion approach for Android malware detection...

Full description

Saved in:

Bibliographic Details
Main Authors:	Altyeb Taha, Ahmed Hamza Osman, Yakubu Suleiman Baguda
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Series:	Technologies
Subjects:	android malware detection fuzzy rank-based fusion machine learning ensemble learning
Online Access:	https://www.mdpi.com/2227-7080/13/2/45
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An extrinsic random-based ensemble approach for android malware detection
by: Nektaria Potha, et al.
Published: (2021-10-01)

A Survey on Android Malware Detection Techniques Using Supervised Machine Learning
by: Safa J. Altaha, et al.
Published: (2024-01-01)

FLSH: A Framework Leveraging Similarity Hashing for Android Malware and Variant Detection
by: Hassan Jalil Hadi, et al.
Published: (2025-01-01)

BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices
by: Emre Şafak, et al.
Published: (2025-05-01)

A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection
by: Sainag Nethala, et al.
Published: (2025-01-01)

Hybrid Android Malware Detection and Classification Using Deep Neural Networks
by: Muhammad Umar Rashid, et al.
Published: (2025-03-01)

LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers
by: Durmus Ozkan Sahin, et al.
Published: (2022-01-01)

Multimodal Deep Learning for Android Malware Classification
by: James Arrowsmith, et al.
Published: (2025-02-01)

Malware-SeqGuard: An Approach Utilizing LSTM and GRU for Effective Detection of Evolving Malware in Android Environments
by: Muhammad Usama Tanveer, et al.
Published: (2025-01-01)

A Robust and Efficient Machine Learning Framework for Enhancing Early Detection of Android Malware
by: Fandi Kurniawan, et al.
Published: (2025-01-01)

Is Malware Detection Needed for Android TV?
by: Gokhan Ozogur, et al.
Published: (2025-03-01)

A Review of Explainable AI for Android Malware Detection and Analysis
by: Maryam Tanha, et al.
Published: (2025-01-01)

Evaluating Lightweight Transformers With Local Explainability for Android Malware Detection
by: Fatima Bourebaa, et al.
Published: (2025-01-01)

CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES
by: abdullah alsraratee, et al.
Published: (2025-04-01)

Active learning model used for android malware detection
by: Md․Habibullah Shakib, et al.
Published: (2025-09-01)

Android Malware Category and Family Identification Using Parallel Machine Learning
by: Ahmed Hashem El Fiky, et al.
Published: (2022-07-01)

Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications
by: Seyeon Park, et al.
Published: (2025-01-01)

Android malware detection using random forest algorithm
by: Samson Isaac, et al.
Published: (2025-05-01)

ISAnWin: inductive generalized zero-shot learning using deep CNN for malware detection across windows and android platforms
by: Umm-e-Hani Tayyab, et al.
Published: (2024-12-01)

Advanced Financial Fraud Malware Detection Method in the Android Environment
by: Jaeho Shin, et al.
Published: (2025-04-01)

Research on Android malware detection method based on multimodal feature fusion
by: Ge Jike, et al.
Published: (2025-01-01)

A Context-Aware Android Malware Detection Approach Using Machine Learning
by: Mohammed N. AlJarrah, et al.
Published: (2022-11-01)

Android malware detection method based on SimHash
by: Bo CHEN, et al.
Published: (2017-11-01)

Android malware detection method based on SimHash
by: Bo CHEN, et al.
Published: (2017-11-01)

Multimodal malware classification using proposed ensemble deep neural network framework
by: Sadia Nazim, et al.
Published: (2025-05-01)

A novel feature selection technique: Detection and classification of Android malware
by: Sandeep Sharma, et al.
Published: (2025-03-01)

Android malware family classification method based on the image of bytecodeConstruction of MDS matrices
by: Yi-min YANG, et al.
Published: (2016-06-01)

A multi-tiered feature selection model for android malware detection based on Feature discrimination and Information Gain
by: Parnika Bhat, et al.
Published: (2022-11-01)

Comparative Analysis of Machine Learning Models for Android Malware Detection
by: Adem Korkmaz, et al.
Published: (2024-06-01)

An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features
by: Abdurraheem Joomye, et al.
Published: (2025-01-01)

PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping
by: Arvind Prasad, et al.
Published: (2025-01-01)

GSIDroid: A Suspicious Subgraph-Driven and Interpretable Android Malware Detection System
by: Hong Huang, et al.
Published: (2025-07-01)

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles
by: Naif Almakayeel
Published: (2024-10-01)

Android malware detection method based on deep neural network
by: Fan CHAO, et al.
Published: (2020-10-01)

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection
by: Yogesh Kumar Sharma, et al.
Published: (2025-01-01)

MALWARE DETECTION CAPABILITIES FOR MOBILE DEVICES RUNNING ANDROID OS BY MONITORING HARDWARE RESOURCE CONSUMPTION
by: Andrew M. Bonch-Bruevich, et al.
Published: (2025-07-01)

Static Analysis-based Detection of Android Malware using Machine Learning Algorithms
by: Omar Emad Saied, et al.
Published: (2025-09-01)

Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset
by: Ines Aulia Latifah, et al.
Published: (2024-11-01)

A Hybrid CNN–BiLSTM Framework Optimized with Bayesian Search for Robust Android Malware Detection
by: Ibrahim Mutambik
Published: (2025-07-01)

A Survey on Adversarial Attacks for Malware Analysis
by: Kshitiz Aryal, et al.
Published: (2025-01-01)