Android malware classification using optimum feature selection and ensemble machine learning

Android malware classification using optimum feature selection and ensemble machine learning

The majority of smartphones on the market run on the Android operating system. Security has been a core concern with this platform since it allows users to install apps from unknown sources. With thousands of apps being produced and launched daily, malware detection using Machine Learning (ML) has a...

Full description

Bibliographic Details
Main Authors:	Rejwana Islam, Moinul Islam Sayed, Sajal Saha, Mohammad Jamal Hossain, Md Abdul Masud
Format:	Article
Language:	English
Published:	KeAi Communications Co., Ltd. 2023-01-01
Series:	Internet of Things and Cyber-Physical Systems
Subjects:	Android Malware Category classification Dynamic analysis Supervised ML Ensemble
Online Access:	http://www.sciencedirect.com/science/article/pii/S2667345223000202

Similar Items

Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification
by: Altyeb Taha, et al.
Published: (2021-11-01)

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

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

Android malware detection with ensemble of androidmanifest features
by: Mohammad Salehi, .
Published: (2019)

Android malware category detection using a novel feature vector-based machine learning model
by: Hashida Haidros Rahima Manzil, et al.
Published: (2023-03-01)

MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection
by: Xusheng Wang, et al.
Published: (2022-03-01)

AndroDFA: Android Malware Classification Based on Resource Consumption
by: Luca Massarelli, et al.
Published: (2020-06-01)

A Hybrid Approach for Android Malware Detection and Family Classification
by: Meghna Dhalaria, et al.
Published: (2021-05-01)

Feature-Based Semi-Supervised Learning Approach to Android Malware Detection
by: Mariam Memon, et al.
Published: (2023-04-01)

On the Evaluation of Android Malware Detectors
by: Hassan Rafiq, et al.
Published: (2018-06-01)

On the Evaluation of Android Malware Detectors
by: Hassan Rafiq, et al.
Published: (2018-06-01)

On the Evaluation of Android Malware Detectors
by: Hassan Rafiq, et al.
Published: (2018-06-01)

Android malware detection with MH-100K: An innovative dataset for advanced research
by: Hendrio Bragança, et al.
Published: (2023-12-01)

An Analysis of Android Malware Classification Services
by: Mohammed Rashed, et al.
Published: (2021-08-01)

PAIRED: An Explainable Lightweight Android Malware Detection System
by: Mohammed M. Alani, et al.
Published: (2022-01-01)

GANG-MAM: GAN based enGine for Modifying Android Malware
by: Renjith G., et al.
Published: (2022-06-01)

BENCHMARKING MACHINE LEARNING ALGORITHMS FOR ANDROID MALWARE DETECTION
by: Somayyeh Fallah, et al.
Published: (2019-12-01)

Android Malware Family Classification and Analysis: Current Status and Future Directions
by: Fahad Alswaina, et al.
Published: (2020-06-01)

Countering Android Malware: A Scalable Semi-Supervised Approach for Family-Signature Generation
by: Andrea Atzeni, et al.
Published: (2018-01-01)

Deep Feature Extraction and Classification of Android Malware Images
by: Jaiteg Singh, et al.
Published: (2020-12-01)

A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection
by: Vasileios Kouliaridis, et al.
Published: (2021-04-01)

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

A Lightweight Multi-Source Fast Android Malware Detection Model
by: Tao Peng, et al.
Published: (2022-05-01)

Android Malware Classification Based on Fuzzy Hashing Visualization
by: Horacio Rodriguez-Bazan, et al.
Published: (2023-11-01)

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis
by: Esra Calik Bayazit, et al.
Published: (2023-01-01)

FAMD: A Fast Multifeature Android Malware Detection Framework, Design, and Implementation
by: Hongpeng Bai, et al.
Published: (2020-01-01)

An Efficient Malware Detection Approach for Malicious Android Application
by: MADIHA AMJAD HUSSAIN, et al.
Published: (2022-04-01)

Cross-device behavioral consistency: Benchmarking and implications for effective android malware detection
by: Alejandro Guerra-Manzanares, et al.
Published: (2022-09-01)

Android Malware Family Classification Method Based on Synthetic Image and Xception Improved Model
by: YU Xingzhan, LU Tianliang, DU Yanhui, WANG Xirui, YANG Cheng
Published: (2023-04-01)

On the evaluation of android malware detectors against code-obfuscation techniques
by: Umair Nawaz, et al.
Published: (2022-06-01)

A Hybrid Analysis-Based Approach to Android Malware Family Classification
by: Chao Ding, et al.
Published: (2021-08-01)

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors
by: Ying-Dar Lin, et al.
Published: (2016-09-01)

Android Malware Detection Based on Factorization Machine
by: Chenglin Li, et al.
Published: (2019-01-01)

The rise of obfuscated Android malware and impacts on detection methods
by: Wael F. Elsersy, et al.
Published: (2022-03-01)

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection
by: Md. Shohel Rana, et al.
Published: (2020-05-01)

FEATURE SELECTIION TECHNIQUE FOR ANDROID MALWARE CLASSIFICATION ON DATA SECTION IMAGE PATTERN /
by: Fauzi Mohd Darus, author 277767, et al.
Published: (2022)

FEATURE SELECTIION TECHNIQUE FOR ANDROID MALWARE CLASSIFICATION ON DATA SECTION IMAGE PATTERN /
by: Fauzi Mohd Darus, author 277767, et al.
Published: (2022)

Android malware dataset construction methodology to minimize bias–variance tradeoff
by: Shinho Lee, et al.
Published: (2022-09-01)

Android Malware Detection Using Kullback-Leibler Divergence
by: Vanessa N. COOPER, et al.
Published: (2015-03-01)

Android Malware Familial Classification Based on DEX File Section Features
by: Yong Fang, et al.
Published: (2020-01-01)