An extrinsic random-based ensemble approach for android malware detection

An extrinsic random-based ensemble approach for android malware detection

Malware detection is a fundamental task and associated with significant applications in humanities, cybersecurity, and social media analytics. In some of the relevant studies, there is substantial evidence that heterogeneous ensembles can provide very reliable solutions, better than any individual v...

Full description

Saved in:

Bibliographic Details
Main Authors:	Nektaria Potha, V. Kouliaridis, G. Kambourakis
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2021-10-01
Series:	Connection Science
Subjects:	mobile malware malware analysis machine learning ensemble learning android security
Online Access:	http://dx.doi.org/10.1080/09540091.2020.1853056
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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)

A Review of Explainable AI for Android Malware Detection and Analysis
by: Maryam Tanha, 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)

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)

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

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

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

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)

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)

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)

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

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

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

An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion
by: Altyeb Taha, 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)

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)

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

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

Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
by: Yadigar Imamverdiyev, et al.
Published: (2025-01-01)

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

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)

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)

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 Review of the Recent Trends in Mobile Malware Evolution, Detection, and Analysis
by: Seetah Almarri, 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)

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

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

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

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

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

Application of deep learning in malware detection: a review
by: Yafei Song, et al.
Published: (2025-04-01)

I-MCM: IoT Malware Counter Measures for Cross-Architecture IoT Malware Detection
by: Ibrahim Gulatas, et al.
Published: (2025-01-01)

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

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

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)