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Android Mobile Malware Detection Using Machine Learning: A Systematic Review
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Android security assessment: A review, taxonomy and research gap study
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Comparative analysis of Android and iOS from security viewpoint
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Malicious application detection in android — A systematic literature review
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Exploring software security approaches in software development lifecycle: A systematic mapping study
inferredcites →https://doi.org/10.1016/j.future.2019.07.070openalex-snapshot
Static malware detection and attribution in android byte-code through an end-to-end deep system
inferredcites →https://doi.org/10.1016/j.infsof.2017.04.001openalex-snapshot
Static analysis of android apps: A systematic literature review
inferredcites →https://doi.org/10.1016/j.infsof.2021.106569openalex-snapshot
MEGDroid: A model-driven event generation framework for dynamic android malware analysis
inferredcites →https://doi.org/10.1016/j.jisa.2020.102483openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jisa.2020.102718openalex-snapshot
Multi-view deep learning for zero-day Android malware detection
inferredcites →https://doi.org/10.1016/j.jnca.2019.102479openalex-snapshot
An efficient reinforcement learning-based Botnet detection approach
inferredcites →https://doi.org/10.1016/j.jnca.2019.102526openalex-snapshot
The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
inferredcites →https://doi.org/10.1016/j.jss.2020.110609openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.pmcj.2016.06.018openalex-snapshot
DroidChain: A novel Android malware detection method based on behavior chains
inferredcites →https://doi.org/10.1016/s1361-3723(20)30028-2openalex-snapshot
Making sense of human threats and errors
inferredcites →https://doi.org/10.1109/access.2017.2771470openalex-snapshot
Machine Learning-Based Malicious Application Detection of Android
inferredcites →https://doi.org/10.1109/access.2018.2835654openalex-snapshot
DroidEnsemble: Detecting Android Malicious Applications With Ensemble of String and Structural Static Features
inferredcites →https://doi.org/10.1109/access.2019.2912200openalex-snapshot
Review of Deep Learning Algorithms and Architectures
inferredcites →https://doi.org/10.1109/access.2019.2919796openalex-snapshot
An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis
inferredcites →https://doi.org/10.1109/access.2020.3002842openalex-snapshot
A Systematic Literature Review of Android Malware Detection Using Static Analysis
inferredcites →https://doi.org/10.1109/access.2020.3006143openalex-snapshot
A Review of Android Malware Detection Approaches Based on Machine Learning
inferredcites →https://doi.org/10.1109/access.2020.3016774openalex-snapshot
Vulnerability Prediction From Source Code Using Machine Learning
inferredcites →https://doi.org/10.1109/access.2021.3063748openalex-snapshot
AdMat: A CNN-on-Matrix Approach to Android Malware Detection and Classification
inferredcites →https://doi.org/10.1109/compcomm.2017.8322752openalex-snapshot
Vulnerability detection with deep learning
inferredcites →https://doi.org/10.1109/comst.2014.2386139openalex-snapshot
Android Security: A Survey of Issues, Malware Penetration, and Defenses
inferredcites →https://doi.org/10.1109/dasc-picom-datacom-cyberscitec.2017.24openalex-snapshot
Detecting Android Malware Based on Extreme Learning Machine
inferredcites →https://doi.org/10.1109/ecace.2019.8679493openalex-snapshot
Analysis and Detection of Malware in Android Applications Using Machine Learning
inferredcites →https://doi.org/10.1109/eit48999.2020.9208339openalex-snapshot
Mobile Security: A Look into Android
inferredcites →https://doi.org/10.1109/eurosp.2018.00040openalex-snapshot
DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccci.2018.8441295openalex-snapshot
HAAMD: Hybrid Analysis for Android Malware Detection
inferredcites →https://doi.org/10.1109/icimcis51567.2020.9354315openalex-snapshot
Hybrid-Based Malware Analysis for Effective and Efficiency Android Malware Detection
inferredcites →https://doi.org/10.1109/icisce.2017.124openalex-snapshot
Research on Android Intent Security Detection Based on Machine Learning
inferredcites →https://doi.org/10.1109/icmla.2018.00120openalex-snapshot
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
inferredcites →https://doi.org/10.1109/icsesp.2018.8376705openalex-snapshot
RanDroid: Android malware detection using random machine learning classifiers
inferredcites →https://doi.org/10.1109/icsme.2017.31openalex-snapshot
Understanding Android Application Programming and Security: A Dynamic Study
inferredcites →https://doi.org/10.1109/infoct.2019.8711179openalex-snapshot
TFDroid: Android Malware Detection by Topics and Sensitive Data Flows Using Machine Learning Techniques
inferredcites →https://doi.org/10.1109/isdfs.2019.8757523openalex-snapshot
Overview of machine learning methods for Android malware identification
inferredcites →https://doi.org/10.1109/msr.2019.00064openalex-snapshot
A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software
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A Survey on Android Malware Detection Techniques Using Machine Learning Algorithms
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inferredcites →https://doi.org/10.1109/sp.2012.16openalex-snapshot
Dissecting Android Malware: Characterization and Evolution
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inferredcites →https://doi.org/10.1109/ssic.2018.8556755openalex-snapshot
A Novel Android Malware Detection Approach Using Operand Sequences
inferredcites →https://doi.org/10.1109/surv.2013.101613.00077openalex-snapshot
Evolution, Detection and Analysis of Malware for Smart Devices
inferredcites →https://doi.org/10.1109/tdsc.2017.2745575openalex-snapshot
Detection of Repackaged Android Malware with Code-Heterogeneity Features
inferredcites →https://doi.org/10.1109/tifs.2017.2656460openalex-snapshot
Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting
inferredcites →https://doi.org/10.1109/tii.2017.2789219openalex-snapshot
Significant Permission Identification for Machine-Learning-Based Android Malware Detection
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
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Guidelines for snowballing in systematic literature studies and a replication in software engineering
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Semantic modelling of Android malware for effective malware comprehension, detection, and classification
inferredcites →https://doi.org/10.1145/3017427openalex-snapshot
The Evolution of Android Malware and Android Analysis Techniques
inferredcites →https://doi.org/10.1145/3021460.3021485openalex-snapshot
Dynamic Permissions based Android Malware Detection using Machine Learning Techniques
inferredcites →https://doi.org/10.1145/3029806.3029823openalex-snapshot
Deep Android Malware Detection
inferredcites →https://doi.org/10.1145/3077286.3077288openalex-snapshot
A Comparison of Features for Android Malware Detection
inferredcites →https://doi.org/10.1145/3092566openalex-snapshot
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques
inferredcites →https://doi.org/10.1145/3094243.3094245openalex-snapshot
Predicting Vulnerable Software Components through Deep Neural Network
inferredcites →https://doi.org/10.1145/3106237.3106294openalex-snapshot
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Machine Learning Methods for Software Vulnerability Detection
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inferredcites →https://doi.org/10.1145/3360774.3360805openalex-snapshot
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inferredcites →https://doi.org/10.1145/3379597.3387469openalex-snapshot
Automatically Granted Permissions in Android apps
inferredcites →https://doi.org/10.1145/3411764.3445616openalex-snapshot
Security Notifications in Static Analysis Tools: Developers’ Attitudes, Comprehension, and Ability to Act on Them
inferredcites →https://doi.org/10.1145/3417113.3422188openalex-snapshot
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inferredcites →https://doi.org/10.1145/3544968openalex-snapshot
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inferredcites →https://doi.org/10.1155/2018/4157156openalex-snapshot
TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
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inferredcites →https://doi.org/10.17762/turcomat.v12i10.4931openalex-snapshot
Feature Engineering based on Hybrid Features for Malware Detection over Android Framework
inferredcites →https://doi.org/10.17781/p002605openalex-snapshot
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inferredcites →https://doi.org/10.18517/ijaseit.10.2.10238openalex-snapshot
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inferredcites →https://doi.org/10.22247/ijcna/2019/187292openalex-snapshot
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inferredcites →https://doi.org/10.24251/hicss.2021.839openalex-snapshot
Machine Learning-Based Android Malware Detection Using Manifest Permissions
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
inferredcites →https://doi.org/10.3390/app10144966openalex-snapshot
Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features
inferredcites →https://doi.org/10.3390/info10100326openalex-snapshot
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inferredcites →https://doi.org/10.3390/info12050185openalex-snapshot
A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection
inferredcites →https://doi.org/10.3390/sym13010035openalex-snapshot
Automatic Malicious Code Classification System through Static Analysis Using Machine Learning
inferredcites →https://doi.org/10.4230/lipics.ecoop.2019.21openalex-snapshot
MagpieBridge: A General Approach to Integrating Static Analyses into IDEs and Editors (Tool Insights Paper)
inferredcites →https://doi.org/10.48550/arxiv.1603.02308openalex-snapshot
Towards Automated Android App Collusion Detection
inferredcites →https://doi.org/10.48550/arxiv.1706.10220openalex-snapshot
6thSense: A Context-aware Sensor-based Attack Detector for Smart Devices
inferredcites →https://doi.org/10.5220/0006256706530662openalex-snapshot
Static and Dynamic Analysis of Android Malware
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AndroVul: a repository for Android security vulnerabilities.
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What Static Analysis Can Utmost Offer for Android Malware Detection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Malware Detection Using The Machine Learning Based Modified Partial Swarm Optimization Approach
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Malware Detection in Mobile Devices Using Hard Voting Ensemble Technique
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Integrating CNN and XGBoost with Synthetic Samples for Advanced Android Malware Detection
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Empowering Android Malware Detection: A Deep Learning Ensemble with Optimal Features
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Malware Detection Technique for Android Devices Using Machine Learning Algorithms
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The State of the Art in Machine Learning-Based Information Security: Identifying the Gaps
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Malware Detection through Memory Dump Analysis by Enhanced Machine Learning Techniques
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Hybrid Deep Learning and Behavioral Analysis for Enhanced Malware Detection in Banking
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Supervised Learning Approaches in Android Malware Detection: Survey and Analysis
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Malware Detection on Android Using Machine Learning
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Securing the Internet of Things: Using Machine Learning for Malware Detection with CIC-MalMem Dataset
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Deep Learning Based Malapps Detection in Android Powered Mobile Cyber-Physical System
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Investigation of Malware & Threat Analysis on APKs Using SVM & ANN Algorithm. -A New Approach
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DroidCleaner: An Android Malware Detector
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Explainable Lightweight Hybrid Ensemble Learning for Android Malware Detection Using Optimized Permission Features
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AI-Driven Framework for Enhancing Digital Well-Being through Smartphone Usage Monitoring Among University Students
inferred← citeshttps://doi.org/10.1109/incet61516.2024.10593208openalex-snapshot
Advancing Android Security: Leveraging Stacking Ensemble and Bioinspired Feature Selection for Efficient Malware Detection
inferred← citeshttps://doi.org/10.1109/incoft60753.2023.10425717openalex-snapshot
Multiclass Classification of Malware using Machine Learning
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Generalizable Detection of Document-Based Phishing Malware: An Explainable Machine Learning Approach on CIC-Trap4Phish
inferred← citeshttps://doi.org/10.1109/scse53661.2021.9568333openalex-snapshot
LYZGen: A mechanism to generate leads from Generation Y and Z by analysing web and social media data
inferred← citeshttps://doi.org/10.1109/scse56529.2022.9905212openalex-snapshot
Detection of IoT Malware Based on Forensic Analysis of Network Traffic Features
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Android Malware Classification Based on Graph Features and Graph Neural Networks
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Detection of Smartphone Permissioned Security Breaches using Machine Learning Techniques
inferred← citeshttps://doi.org/10.1145/3488932.3527290openalex-snapshot
Developing Secured Android Applications by Mitigating Code Vulnerabilities with Machine Learning
inferred← citeshttps://doi.org/10.1145/3556974openalex-snapshot
Android Source Code Vulnerability Detection: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1155/2022/8621083openalex-snapshot
A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection
inferred← citeshttps://doi.org/10.1186/s13635-024-00182-3openalex-snapshot
Static analysis framework for permission-based dataset generation and android malware detection using machine learning
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10806500/v1openalex-snapshot
Enhancing the Robustness of Android Malware Detection Systems Against Adversarial Attacks: A Systematic Review of Defense Strategies and Emerging Challenges
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4114338/v1openalex-snapshot
OHMN: Android Malware Detection Method Using Opcode Highway Memory Network
inferred← citeshttps://doi.org/10.2197/ipsjjip.32.298openalex-snapshot
JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
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Android Malware Detection Using Unified Evolutionary Multi-Domain Cybersecurity Detection Architecture
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METHODS AND MEANS OF DETECTION OF CYBER INCIDENTS ON MOBILE TERMINAL DEVICES IN WELL-KNOWN INFORMATION AND COMMUNICATION SYSTEMS
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Detection of Android Based Applications with Traditional Metaheuristic Algorithms
inferred← citeshttps://doi.org/10.30564/aia.v6i1.6718openalex-snapshot
Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions
inferred← citeshttps://doi.org/10.32604/iasc.2023.041130openalex-snapshot
Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN)
inferred← citeshttps://doi.org/10.3390/a19080688openalex-snapshot
XAI-Guided Graph-Based Feature Engineering and Heterogeneous Ensemble Learning for Android Malware Detection
inferred← citeshttps://doi.org/10.3390/app12136672openalex-snapshot
MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm
inferred← citeshttps://doi.org/10.3390/app13148056openalex-snapshot
A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques
inferred← citeshttps://doi.org/10.3390/books978-3-0365-7174-4openalex-snapshot
High Accuracy Detection of Mobile Malware Using Machine Learning
inferred← citeshttps://doi.org/10.3390/computers15090624openalex-snapshot
Deep Learning Approaches for Phishing Detection: A Systematic Review with a Focus on Mobile Deployment, Explainability, and Temporal Modelling
inferred← citeshttps://doi.org/10.3390/electronics11030486openalex-snapshot
A Novel Android Botnet Detection System Using Image-Based and Manifest File Features
inferred← citeshttps://doi.org/10.3390/electronics11111798openalex-snapshot
DroidFDR: Automatic Classification of Android Malware Using Model Checking
inferred← citeshttps://doi.org/10.3390/electronics12051197openalex-snapshot
Detection of Vulnerabilities by Incorrect Use of Variable Using Machine Learning
inferred← citeshttps://doi.org/10.3390/electronics12061408openalex-snapshot
High Accuracy Detection of Mobile Malware Using Machine Learning
inferred← citeshttps://doi.org/10.3390/electronics15102167openalex-snapshot
AI-Driven Rule-Based Feature Scoring for Explainable and Adversarially Robust Android Malware Detection
inferred← citeshttps://doi.org/10.3390/fi17120532openalex-snapshot
A Resilient Deep Learning Framework for Mobile Malware Detection: From Architecture to Deployment
inferred← citeshttps://doi.org/10.3390/jcp5030058openalex-snapshot
A Comprehensive Analysis of Evolving Permission Usage in Android Apps: Trends, Threats, and Ecosystem Insights
inferred← citeshttps://doi.org/10.3390/s22176562openalex-snapshot
Lightweight On-Device Detection of Android Malware Based on the Koodous Platform and Machine Learning
inferred← citeshttps://doi.org/10.36948/ijfmr.2024.v06i03.20891openalex-snapshot
Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques
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Employing combined spatial and frequency domain image features for machine learning-based malware detection
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Autonomous Defending Approaches Based on Machine Learning for Android Malware Detection
inferred← citeshttps://doi.org/10.4018/ijiit.329956openalex-snapshot
Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks
inferred← citeshttps://doi.org/10.48047/afjbs.6.9.2024.2556-2560openalex-snapshot
MACHINE LEARNING BASED MALWARE EVALUATION FOR ANDROID
inferred← citeshttps://doi.org/10.51574/ijrer.v4i2.2835openalex-snapshot
Development of Interactive Learning Media on the Material of Relations and Functions in Middle School Students
inferred← citeshttps://doi.org/10.5220/0012060400003555openalex-snapshot
Labelled Vulnerability Dataset on Android Source Code (LVDAndro) to Develop AI-Based Code Vulnerability Detection Models
inferred← citeshttps://doi.org/10.55041/ijsrem32180openalex-snapshot
Mobile Malware Detection using Machine Learning Classifiers
inferred← citeshttps://doi.org/10.55248/gengpi.5.0324.0805openalex-snapshot
Evolution of Android Malware
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Can machine learning be used to detect malware?
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ViTDroid and Hybrid Models for Effective Android and IoT Malware Detection
inferred← citeshttps://doi.org/10.7454/mst.v6i1.6openalex-snapshot
PEMBUATAN PADUAN (Ti1-xAlx)N DENGAN TEKNIK PLASMA CVD DARI BUBUK AlCl3, GAS N2 DAN H2, SERTA LARUTAN TiCl4
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BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices
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