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Hybrid sequence‐based Android malware detection using natural language processing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cpe.5308openalex-snapshot
Opcode sequence analysis of Android malware by a convolutional neural network
inferredcites →https://doi.org/10.1007/978-3-319-04283-1_6openalex-snapshot
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
inferredcites →https://doi.org/10.1007/978-3-319-11203-9_10openalex-snapshot
DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications
inferredcites →https://doi.org/10.1007/978-981-15-3418-8_12openalex-snapshot
ByteDroid: Android Malware Detection Using Deep Learning on Bytecode Sequences
inferredcites →https://doi.org/10.1007/978-981-15-4825-3_25openalex-snapshot
Android Malware Detector Based on Sequences of System Calls and Bidirectional Recurrent Networks
inferredcites →https://doi.org/10.1007/s00500-019-04589-wopenalex-snapshot
DroidDeep: using Deep Belief Network to characterize and detect android malware
inferredcites →https://doi.org/10.1007/s10844-010-0148-xopenalex-snapshot
“Andromaly”: a behavioral malware detection framework for android devices
inferredcites →https://doi.org/10.1016/j.cose.2017.11.007openalex-snapshot
Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach
inferredcites →https://doi.org/10.1016/j.cose.2019.101663openalex-snapshot
DL-Droid: Deep learning based android malware detection using real devices
inferredcites →https://doi.org/10.1016/j.diin.2018.01.007openalex-snapshot
MalDozer: Automatic framework for android malware detection using deep learning
inferredcites →https://doi.org/10.1016/j.diin.2019.01.017openalex-snapshot
MalDy: Portable, data-driven malware detection using natural language processing and machine learning techniques on behavioral analysis reports
inferredcites →https://doi.org/10.1016/j.future.2018.11.021openalex-snapshot
A machine learning based approach to detect malicious android apps using discriminant system calls
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.2020.106291openalex-snapshot
A study of run-time behavioral evolution of benign versus malicious apps in android
inferredcites →https://doi.org/10.1016/j.jisa.2018.07.003openalex-snapshot
SWORD: Semantic aWare andrOid malwaRe Detector
inferredcites →https://doi.org/10.1016/j.neucom.2019.01.078openalex-snapshot
Bidirectional LSTM with attention mechanism and convolutional layer for text classification
inferredcites →https://doi.org/10.1109/ares.2015.57openalex-snapshot
Effectiveness of Opcode ngrams for Detection of Multi Family Android Malware
inferredcites →https://doi.org/10.1109/compsac.2015.103openalex-snapshot
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis
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/icc.2014.6883436openalex-snapshot
Using opcode-sequences to detect malicious Android applications
inferredcites →https://doi.org/10.1109/icct.2018.8600052openalex-snapshot
Deep Neural Network Based on Android Mobile Malware Detection System Using Opcode Sequences
inferredcites →https://doi.org/10.1109/infcomw.2017.8116346openalex-snapshot
TextDroid: Semantics-based detection of mobile malware using network flows
inferredcites →https://doi.org/10.1109/infcomw.2017.8116425openalex-snapshot
App behavioral analysis using system calls
inferredcites →https://doi.org/10.1109/iscc47284.2019.8969656openalex-snapshot
Android Malware Family Classification Based on Sensitive Opcode Sequence
inferredcites →https://doi.org/10.1109/malware.2010.5665792openalex-snapshot
An Android Application Sandbox system for suspicious software detection
inferredcites →https://doi.org/10.1109/malware.2016.7888739openalex-snapshot
DySign: dynamic fingerprinting for the automatic detection of android malware
inferredcites →https://doi.org/10.1109/tdsc.2016.2536605openalex-snapshot
MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention
inferredcites →https://doi.org/10.1109/tifs.2018.2866319openalex-snapshot
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
inferredcites →https://doi.org/10.1109/tifs.2019.2932228openalex-snapshot
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
inferredcites →https://doi.org/10.1109/trustcom.2016.0070openalex-snapshot
A Deep Learning Approach to Android Malware Feature Learning and Detection
inferredcites →https://doi.org/10.1145/2591971.2592003openalex-snapshot
A measurement study of google play
inferredcites →https://doi.org/10.1145/2660267.2660359openalex-snapshot
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs
inferredcites →https://doi.org/10.1145/2804345.2804349openalex-snapshot
Detecting Android malware using sequences of system calls
inferredcites →https://doi.org/10.1145/3029806.3029823openalex-snapshot
Deep Android Malware Detection
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
inferredcites →https://doi.org/10.17485/ijst/2016/v9i21/90273openalex-snapshot
System Call Analysis of Android Malware Families
inferredcites →https://doi.org/10.3115/v1/d14-1162openalex-snapshot
Glove: Global Vectors for Word Representation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ett.4840openalex-snapshot
MUDROID: Android malware detection and classification based on permission and behavior for autonomous vehicles
inferred← citeshttps://doi.org/10.1002/int.22712openalex-snapshot
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Label distribution‐based noise correction for multiclass crowdsourcing
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Feature autoencoder for detecting adversarial examples
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RDAD: A reconstructive and discriminative anomaly detection model based on transformer
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Security of federated learning for cloud‐edge intelligence collaborative computing
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Reward optimization of spatial crowdsourcing for coalition‐based maintenance task
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Towards robust and stealthy communication for wireless intelligent terminals
inferred← citeshttps://doi.org/10.1007/978-3-031-08223-8_17openalex-snapshot
TrojanDroid: Android Malware Detection for Trojan Discovery Using Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-031-89360-5_9openalex-snapshot
AnGSL: Graph-Based Android Malware Detection Method
inferred← citeshttps://doi.org/10.1007/978-3-032-10250-8_16openalex-snapshot
AndroV-Rank: Optimized Android Malware Detection Through VIKOR-Ranked Hybrid Features
inferred← citeshttps://doi.org/10.1007/978-3-032-32767-3_22openalex-snapshot
Localizing Hot Pixels in Android Malware Images
inferred← citeshttps://doi.org/10.1007/978-981-97-1459-9_1openalex-snapshot
Introduction of Android Malware Detection
inferred← citeshttps://doi.org/10.1007/978-981-99-0272-9_18openalex-snapshot
Android Malware Detection: A Literature Review
inferred← citeshttps://doi.org/10.1007/s10044-025-01516-8openalex-snapshot
ViTGuard: a synergistic approach to malware detection using vision transformers and genetic algorithms optimization
inferred← citeshttps://doi.org/10.1007/s11042-024-18802-yopenalex-snapshot
An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks
inferred← citeshttps://doi.org/10.1007/s11235-022-00937-8openalex-snapshot
Dynamic permission access control model based on privacy protection
inferred← citeshttps://doi.org/10.1007/s11704-022-2225-zopenalex-snapshot
Attribute augmentation-based label integration for crowdsourcing
inferred← citeshttps://doi.org/10.1007/s11770-025-1366-xopenalex-snapshot
A software imaging-based inversion framework for Android malware detection
inferred← citeshttps://doi.org/10.1007/s12046-025-02843-6openalex-snapshot
Vulnerability analysis of android based FinTech apps in India
inferred← citeshttps://doi.org/10.1007/s12652-022-04020-7openalex-snapshot
A blockchain-based access control protocol for secure resource sharing with mobile edge-cloud collaboration
inferred← citeshttps://doi.org/10.1007/s41870-023-01392-7openalex-snapshot
Stacking-based ensemble model for malware detection in android devices
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2023.108804openalex-snapshot
DroidEncoder: Malware detection using auto-encoder based feature extractor and machine learning algorithms
inferred← citeshttps://doi.org/10.1016/j.cose.2022.102757openalex-snapshot
Concept drift and cross-device behavior: Challenges and implications for effective android malware detection
inferred← citeshttps://doi.org/10.1016/j.cose.2022.102915openalex-snapshot
An Android Malware Detection and Classification Approach Based on Contrastive Lerning
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103654openalex-snapshot
Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives
inferred← citeshttps://doi.org/10.1016/j.cosrev.2026.100980openalex-snapshot
Hybrid analysis in android malware detection: A systematic and comparative review
inferred← citeshttps://doi.org/10.1016/j.infsof.2026.108012openalex-snapshot
Android malware detection by using graph optimization of static features based on pre-trained language models
inferred← citeshttps://doi.org/10.1016/j.ins.2022.04.008openalex-snapshot
BSM-ether: Bribery selfish mining in blockchain-based healthcare systems
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A novel approach based on adaptive online analysis of encrypted traffic for identifying Malware in IIoT
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Strengthening LLM ecosystem security: Preventing mobile malware from manipulating LLM-based applications
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WHGDroid: Effective android malware detection based on weighted heterogeneous graph
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MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling
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Microservice combination optimisation based on improved gray wolf algorithm
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Android Malware Detection in Bytecode Level Using TF-IDF and XGBoost
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CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification
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ChatGPT-driven machine learning code generation for android malware detection
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Analyzing and Explaining Black-Box Models for Online Malware Detection
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Hybrid Android Malware Detection: A Review of Heuristic-Based Approach
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TRConv: Multi-Platform Malware Classification via Target Regulated Convolutions
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Enhancing Malware Detection Using “Genetic Markers” and Machine Learning
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HGNNDroid: Android Malware Detection Based on Heterogeneous Graph Neural Network
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MKPL: Multi-dimensional Knowledge-embedded Prompt Learning for Few-shot Malware Family Recognition
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PET-Droid: Android Malware Detection Using Static Analysis
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NLP and ML Synergy: A Novel Approach in Botnet Detection from Sandbox Artifacts
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A Graph Neural Network Framework for Dynamic Malware Detection Using Api Calls and Lightweight Containers
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Malware Detection in Android Application using Static Permission
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Characterizing and Classifying Android Malware: A High-Level Feature Approach
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