A Robust and Efficient Machine Learning Framework for Enhancing Early Detection of Android Malware

A Robust and Efficient Machine Learning Framework for Enhancing Early Detection of Android Malware

The advancement of information technology has introduced new challenges in cybersecurity, especially related to the Android platform which is the main target of malicious software (malware) attacks. The National Cyber and Crypto Agency (BSSN) of Indonesia reported millions of incidents involving And...

Full description

Saved in:

Bibliographic Details
Main Authors:	Fandi Kurniawan, Deris Stiawan, Darius Antoni, Mohd Yazid Idris, Rahmat Budiarto
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Access
Subjects:	Reverse engineering malware android machine learning malware detection static malware analysis
Online Access:	https://ieeexplore.ieee.org/document/11082145/
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications
by: Seyeon Park, 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)

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

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

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)

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

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)

A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection
by: Sainag Nethala, 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 Hybrid CNN–BiLSTM Framework Optimized with Bayesian Search for Robust Android Malware Detection
by: Ibrahim Mutambik
Published: (2025-07-01)

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

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

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

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

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

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

A Review of the Recent Trends in Mobile Malware Evolution, Detection, and Analysis
by: Seetah Almarri, et al.
Published: (2025-01-01)

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

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)

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)

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

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

Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning
by: Wafaa Al-Kahla, et al.
Published: (2024-01-01)

HoneyBow:an automated malware collection tool based on the high-interaction honeypot principle
by: ZHUGE Jian-wei1, et al.
Published: (2007-01-01)

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)

Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges
by: Harikha Manthena, et al.
Published: (2025-01-01)

MALVADA: A framework for generating datasets of malware execution traces
by: Razvan Raducu, et al.
Published: (2025-05-01)

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

Multimodal and Explainable Android Adware Classification Techniques
by: Momoreoluwa Ayinde, et al.
Published: (2024-05-01)

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

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

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

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)