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DL-Droid: Deep learning based android malware detection using real devices

https://doi.org/10.1016/j.cose.2019.101663

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-04283-1_6openalex-snapshot

DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android

inferredcites →https://doi.org/10.1007/978-3-642-33704-8_21openalex-snapshot

MADAM: A Multi-level Anomaly Detector for Android Malware

inferredcites →https://doi.org/10.1007/s10207-015-0310-0openalex-snapshot

New facets of mobile botnet: architecture and evaluation

inferredcites →https://doi.org/10.1007/s10844-010-0148-xopenalex-snapshot

“Andromaly”: a behavioral malware detection framework for android devices

inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot

Deep learning

inferredcites →https://doi.org/10.1049/iet-ifs.2014.0099openalex-snapshot

High accuracy android malware detection using ensemble learning

inferredcites →https://doi.org/10.1109/access.2019.2893871openalex-snapshot

Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems

inferredcites →https://doi.org/10.1109/ase.2015.89openalex-snapshot

Automated Test Input Generation for Android: Are We There Yet? (E)

inferredcites →https://doi.org/10.1109/asonam.2018.8508306openalex-snapshot

Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

inferredcites →https://doi.org/10.1109/cybersecpods.2016.7502343openalex-snapshot

N-opcode analysis for android malware classification and categorization

inferredcites →https://doi.org/10.1109/cybersecpods.2017.8074845openalex-snapshot

Improving dynamic analysis of android apps using hybrid test input generation

inferredcites →https://doi.org/10.1109/cybersecurity41163.2017openalex-snapshot

2017 International Conference on Cyber Security And Protection Of Digital Services (Cyber Security)

inferredcites →https://doi.org/10.1109/iceei.2015.7352513openalex-snapshot

Malware detection on Android smartphones using API class and machine learning

inferredcites →https://doi.org/10.1109/iceei36517.2015openalex-snapshot

2015 International Conference on Electrical Engineering and Informatics (ICEEI)

inferredcites →https://doi.org/10.1109/icse-c.2017.8openalex-snapshot

DroidBot: a lightweight UI-Guided test input generator for android

inferredcites →https://doi.org/10.1109/icse-c41700.2017openalex-snapshot

2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)

inferredcites →https://doi.org/10.1109/ictai.2013.53openalex-snapshot

Machine Learning for Android Malware Detection Using Permission and API Calls

inferredcites →https://doi.org/10.1109/sai.2015.7237302openalex-snapshot

Android malware detection: An eigenspace analysis approach

inferredcites →https://doi.org/10.1109/sai35119.2015openalex-snapshot

2015 Science and Information Conference (SAI)

inferredcites →https://doi.org/10.1109/tcyb.2017.2777960openalex-snapshot

DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection

inferredcites →https://doi.org/10.1109/tdsc.2014.2355839openalex-snapshot

A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code

inferredcites →https://doi.org/10.1109/tifs.2017.2687880openalex-snapshot

DAPASA: Detecting Android Piggybacked Apps Through Sensitive Subgraph Analysis

inferredcites →https://doi.org/10.1109/tst.2016.7399288openalex-snapshot

Droiddetector: android malware characterization and detection using deep learning

inferredcites →https://doi.org/10.1109/wiw.2016.040openalex-snapshot

Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs

inferredcites →https://doi.org/10.1109/wiw40465.2016openalex-snapshot

2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW)

inferredcites →https://doi.org/10.1145/1037107.1037111openalex-snapshot

ACM SIGCOMM computer communication review

inferredcites →https://doi.org/10.1145/2351676openalex-snapshot

Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering

inferredcites →https://doi.org/10.1145/2351676.2351717openalex-snapshot

Using GUI ripping for automated testing of Android applications

inferredcites →https://doi.org/10.1145/2393596openalex-snapshot

Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering

inferredcites →https://doi.org/10.1145/2393596.2393666openalex-snapshot

Automated concolic testing of smartphone apps

inferredcites →https://doi.org/10.1145/2435349.2435379openalex-snapshot

AppsPlayground

inferredcites →https://doi.org/10.1145/2491411openalex-snapshot

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

inferredcites →https://doi.org/10.1145/2491411.2491450openalex-snapshot

Dynodroid: an input generation system for Android apps

inferredcites →https://doi.org/10.1145/2494522openalex-snapshot

TaintDroid

inferredcites →https://doi.org/10.1145/2509136.2509549openalex-snapshot

Targeted and depth-first exploration for systematic testing of android apps

inferredcites →https://doi.org/10.1145/2509136.2509552openalex-snapshot

Guided GUI testing of android apps with minimal restart and approximate learning

inferredcites →https://doi.org/10.1145/2544173.2509549openalex-snapshot

Targeted and depth-first exploration for systematic testing of android apps

inferredcites →https://doi.org/10.1145/2594368openalex-snapshot

Proceedings of the 12th annual international conference on Mobile systems, applications, and services

inferredcites →https://doi.org/10.1145/2594368.2594390openalex-snapshot

PUMA

inferredcites →https://doi.org/10.1145/2619239.2631434openalex-snapshot

Droid-Sec

inferredcites →https://doi.org/10.1145/3029806openalex-snapshot

Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy

inferredcites →https://doi.org/10.1145/3029806.3029823openalex-snapshot

Deep Android Malware Detection

inferredcites →https://doi.org/10.1145/3041008openalex-snapshot

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics

inferredcites →https://doi.org/10.1145/3041008.3041010openalex-snapshot

EMULATOR vs REAL PHONE

inferredcites →https://doi.org/10.1145/3110025.3116211openalex-snapshot

Deep Neural Networks for Automatic Android Malware Detection

inferredcites →https://doi.org/10.1186/s13635-019-0087-1openalex-snapshot

Machine learning-based dynamic analysis of Android apps with improved code coverage

inferredcites →https://doi.org/10.14722/ndss.2014.23039openalex-snapshot

A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks

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/10.14722/ndss.2015.23145openalex-snapshot

CopperDroid: Automatic Reconstruction of Android Malware Behaviors

inferredcites →https://doi.org/10.22619/ijcsa.2016.1001011openalex-snapshot

N-gram Opcode Analysis for Android Malware Detection

inferredcites →https://doi.org/10.22619/ijcsa.2016.100111openalex-snapshot

N-gram Opcode Analysis for Android Malware Detection

inferredcites →https://doi.org/10.48550/arxiv.1503.07217openalex-snapshot

Automated Test Input Generation for Android: Are We There Yet?

inferredcites →https://doi.org/10.48550/arxiv.1703.10926openalex-snapshot

EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

inferredcites →https://doi.org/10.48550/arxiv.1712.08996openalex-snapshot

Android Malware Detection using Deep Learning on API Method Sequences

inferredcites →https://doi.org/10.5555/1924943.1924971openalex-snapshot

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Fuzzy Set Theory-Based Cutting-Edge Mobile Malware Detection

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TL‐GNN : Android Malware Detection Using Transfer Learning

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Deep malware hunter based unrivaled malware detection schema thru cache retrospective empiricism

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A novel CNN‐based approach for detection and classification of DDoS attacks

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Hybrid sequence‐based Android malware detection using natural language processing

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Android malware detection using network traffic based on sequential deep learning models

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Machine Learning Based Android Vulnerability Detection: A Roadmap

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Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

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A Comprehensive Approach to Android Malware Detection Using Machine Learning

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Revisiting the Approaches, Datasets and Evaluation Parameters to Detect Android Malware: A Comparative Study from State-of-Art

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IFIFDroid: Important Features Identification Framework in Android Malware Detection

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DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode

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Handling Novel Mobile Malware Attacks with Optimised Machine Learning Based Detection and Classification Models

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Familial Analysis of Malicious Android Apps Controlling IOT Devices

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On the Android Malware Detection System Based on Deep Learning

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Systematic Review on Various Techniques of Android Malware Detection

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Towards Optimizing Malware Detection: An Approach Based on Generative Adversarial Networks and Transformers

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Android Malware Detection Using BERT

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Unrestricted Black-Box Adversarial Attack Using GAN with Limited Queries

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Learning Discriminative Representations for Malware Family Classification

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Android Operating System

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Android Malware Detection Using Artificial Intelligence

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MDLDroid: Multimodal Deep Learning Based Android Malware Detection

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Malware Attack Detection in Vehicle Cyber Physical System for Planning and Control Using Deep Learning

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Intelligent Methods for Android Malware Detection: A Survey

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AnGSL: Graph-Based Android Malware Detection Method

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Enhancing Mobile Security Through Biometric Authentication and Variation Autoencoders for Zero-Day Attack Detection

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A Comparative Study of Machine Learning Models for Two-Tier Android Malware Classification with Dynamic Behavioral Analysis

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A Hybrid Feature Selection Approach-Based Android Malware Detection Framework Using Machine Learning Techniques

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Sniffing Android Malware Using Deep Learning

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Towards a Static and Dynamic Features-Based Framework for Android Vulnerabilities Detection

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Hybrid Approach of Modified IWD and Machine Learning Techniques for Android Malware Detection

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A Computationally Inexpensive Method Based on Transfer Learning for Mobile Malware Detection

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Machine-Learning-Driven Android Malware Detection: Techniques and Performance Analysis

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Android Malware Detection Based on Permissions and API Calls Using Ensemble Based Model

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Android Malware Classification Based on Cross-Attention Mechanism with Multiscale CNN and GCN

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Android Malware Detection Using Learning Techniques: A Review

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State-of-the-art survey of artificial intelligent techniques for IoT security

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GDroid: Android malware detection and classification with graph convolutional network

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Android malware detection via an app similarity graph

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KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization

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A Multi-Perspective malware detection approach through behavioral fusion of API call sequence

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FCSCNN: Feature centralized Siamese CNN-based android malware identification

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Robust deep learning early alarm prediction model based on the behavioural smell for android malware

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AdStop: Efficient flow-based mobile adware detection using machine learning

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Concept drift and cross-device behavior: Challenges and implications for effective android malware detection

inferred← citeshttps://doi.org/10.1016/j.cose.2022.102835openalex-snapshot

On the relativity of time: Implications and challenges of data drift on long-term effective android malware detection

inferred← citeshttps://doi.org/10.1016/j.cose.2023.103385openalex-snapshot

A new deep boosted CNN and ensemble learning based IoT malware detection

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.cose.2024.103807openalex-snapshot

GSEDroid: GNN-based Android malware detection framework using lightweight semantic embedding

inferred← citeshttps://doi.org/10.1016/j.cose.2025.104361openalex-snapshot

M2FD: Mobile malware federated detection under concept drift

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The silence of the phishers: Early-stage voice phishing detection with runtime permission requests

inferred← citeshttps://doi.org/10.1016/j.cose.2025.104499openalex-snapshot

Multi-dimensional assessment for Android application security based on users’ evaluation

inferred← citeshttps://doi.org/10.1016/j.cose.2026.104845openalex-snapshot

Dapadv: Differentiated adversarial perturbation generation method in problem space for android malware detection

inferred← citeshttps://doi.org/10.1016/j.cose.2026.105060openalex-snapshot

A bibliometric retrospective of Computers & Security

inferred← citeshttps://doi.org/10.1016/j.cosrev.2021.100373openalex-snapshot

Malicious application detection in android — A systematic literature review

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.csa.2023.100014openalex-snapshot

A review of deep learning models to detect malware in Android applications

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117833openalex-snapshot

DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118404openalex-snapshot

Behaviour analysis of inter-app communication using a lightweight monitoring app for malware detection

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.119593openalex-snapshot

An effective end-to-end android malware detection method

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121125openalex-snapshot

SeGDroid: An Android malware detection method based on sensitive function call graph learning

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121155openalex-snapshot

MalDetect: A classifier fusion approach for detection of android malware

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121617openalex-snapshot

Android malware detection method based on graph attention networks and deep fusion of multimodal features

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122255openalex-snapshot

Detection approaches for android malware: Taxonomy and review analysis

inferred← citeshttps://doi.org/10.1016/j.fsidi.2021.301168openalex-snapshot

RansomDroid: Forensic analysis and detection of Android Ransomware using unsupervised machine learning technique

inferred← citeshttps://doi.org/10.1016/j.fsidi.2023.301564openalex-snapshot

cRGB_Mem: At the intersection of memory forensics and machine learning

inferred← citeshttps://doi.org/10.1016/j.future.2021.02.015openalex-snapshot

Research on unsupervised feature learning for Android malware detection based on Restricted Boltzmann Machines

inferred← citeshttps://doi.org/10.1016/j.future.2021.11.030openalex-snapshot

A study on malicious software behaviour analysis and detection techniques: Taxonomy, current trends and challenges

inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e40699openalex-snapshot

Impact of AI on the Cyber Kill Chain: A Systematic Review

inferred← citeshttps://doi.org/10.1016/j.icte.2024.03.005openalex-snapshot

A study of the relationship of malware detection mechanisms using Artificial Intelligence

inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104388openalex-snapshot

A multimodal fusion method for android malware detection based on modal independence preservation

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.iot.2024.101258openalex-snapshot

Image-based malware analysis for enhanced IoT security in smart cities

inferred← citeshttps://doi.org/10.1016/j.iot.2024.101482openalex-snapshot

RAX-ClaMal: Dynamic Android malware classification based on RAX register values

inferred← citeshttps://doi.org/10.1016/j.iswa.2023.200318openalex-snapshot

DL-AMDet: Deep learning-based malware detector for android

inferred← citeshttps://doi.org/10.1016/j.jestch.2024.101945openalex-snapshot

FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications

inferred← citeshttps://doi.org/10.1016/j.jisa.2021.102751openalex-snapshot

Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment

inferred← citeshttps://doi.org/10.1016/j.jisa.2021.102929openalex-snapshot

Android mobile malware detection using fuzzy AHP

inferred← citeshttps://doi.org/10.1016/j.jisa.2021.103057openalex-snapshot

Deep multi-task learning for malware image classification

inferred← citeshttps://doi.org/10.1016/j.jisa.2023.103486openalex-snapshot

AIM: An Android Interpretable Malware detector based on application class modeling

inferred← citeshttps://doi.org/10.1016/j.jnca.2024.104035openalex-snapshot

Android malware defense through a hybrid multi-modal approach

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113833openalex-snapshot

An intelligent zero-day attack detection system using unsupervised machine learning for enhancing cyber security

inferred← citeshttps://doi.org/10.1016/j.measen.2023.100955openalex-snapshot

Automated malware detection using machine learning and deep learning approaches for android applications

inferred← citeshttps://doi.org/10.1016/j.mlwa.2022.100357openalex-snapshot

Cross-device behavioral consistency: Benchmarking and implications for effective android malware detection

inferred← citeshttps://doi.org/10.1016/j.procs.2022.11.027openalex-snapshot

Transfer Learning Approach for Malware Images Classification on Android Devices Using Deep Convolutional Neural Network

inferred← citeshttps://doi.org/10.1016/j.teler.2024.100130openalex-snapshot

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

inferred← citeshttps://doi.org/10.1038/s41598-024-60982-yopenalex-snapshot

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

inferred← citeshttps://doi.org/10.1038/s41598-026-47985-7openalex-snapshot

Intelligent malware detection on Android smartphones via a hybrid approach using gradient boosting and convolutional neural network

inferred← citeshttps://doi.org/10.1049/cmu2.12265openalex-snapshot

MalDroid: Secure DL‐enabled intelligent malware detection framework

inferred← citeshttps://doi.org/10.1049/cmu2.12754openalex-snapshot

An effective attention and residual network for malware detection

inferred← citeshttps://doi.org/10.1049/ise2.12082openalex-snapshot

DeepCatra: Learning flow‐ and graph‐based behaviours for Android malware detection

inferred← citeshttps://doi.org/10.1049/ise2/5572223openalex-snapshot

AEDroid: Adaptive Enhanced Android Malware Detection‐Based on Interpretability of Deep Learning

inferred← citeshttps://doi.org/10.1063/5.0109147openalex-snapshot

A deep learning based model for detection of Android malwares using PCA over physical devices

inferred← citeshttps://doi.org/10.1063/5.0126116openalex-snapshot

Text preprocessing for optimal accuracy in Indonesian sentiment analysis using a deep learning model with word embedding

inferred← citeshttps://doi.org/10.1063/5.0221856openalex-snapshot

Android malware: Understanding the growing threat landscape and safeguarding measures

inferred← citeshttps://doi.org/10.1063/5.0234163openalex-snapshot

Detecting Android attacks based on deep learning techniques: Status and future directions

inferred← citeshttps://doi.org/10.1063/5.0259063openalex-snapshot

Exploring the effectiveness of machine learning and - Based methods in identifying Malware threats and the hybrid approach potentials: A scoping review

inferred← citeshttps://doi.org/10.1063/5.0298066openalex-snapshot

CYMO – A novel mobile application for attack detection and prevention

inferred← citeshttps://doi.org/10.1080/01969722.2022.2068226openalex-snapshot

Scalable Malware Detection System Using Distributed Deep Learning

inferred← citeshttps://doi.org/10.1093/comjnl/bxae114openalex-snapshot

ChatGPT-driven machine learning code generation for android malware detection

inferred← citeshttps://doi.org/10.1093/comjnl/bxag055openalex-snapshot

HealthDroid: malware detection in healthcare Android application using BERT

inferred← citeshttps://doi.org/10.1108/ijwis-03-2024-0095openalex-snapshot

A static method for detecting android malware based on directed API call

inferred← citeshttps://doi.org/10.1109/access.2020.3009819openalex-snapshot

A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware

inferred← citeshttps://doi.org/10.1109/access.2020.3028370openalex-snapshot

Review of Android Malware Detection Based on Deep Learning

inferred← citeshttps://doi.org/10.1109/access.2021.3055427openalex-snapshot

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

inferred← citeshttps://doi.org/10.1109/access.2021.3079370openalex-snapshot

A Dynamic Robust DL-Based Model for Android Malware Detection

inferred← citeshttps://doi.org/10.1109/access.2021.3123187openalex-snapshot

Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

inferred← citeshttps://doi.org/10.1109/access.2021.3123791openalex-snapshot

AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

inferred← citeshttps://doi.org/10.1109/access.2022.3155695openalex-snapshot

A Malware Detection Approach Using Autoencoder in Deep Learning

inferred← citeshttps://doi.org/10.1109/access.2023.3296606openalex-snapshot

A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques

inferred← citeshttps://doi.org/10.1109/access.2024.3377658openalex-snapshot

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

inferred← citeshttps://doi.org/10.1109/access.2024.3390612openalex-snapshot

Dynamic Security Analysis on Android: A Systematic Literature Review

inferred← citeshttps://doi.org/10.1109/access.2024.3434629openalex-snapshot

Advanced Machine Learning Based Malware Detection Systems

inferred← citeshttps://doi.org/10.1109/access.2024.3485706openalex-snapshot

A Survey on Android Malware Detection Techniques Using Supervised Machine Learning

inferred← citeshttps://doi.org/10.1109/access.2024.3486094openalex-snapshot

Performance-Oriented and Sustainability-Oriented Design of an Effective Android Malware Detector

inferred← citeshttps://doi.org/10.1109/access.2025.3550124openalex-snapshot

Android Malware Detection Method Combining Multi-Frequency Features and Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1109/access.2025.3552070openalex-snapshot

An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features

inferred← citeshttps://doi.org/10.1109/access.2025.3594087openalex-snapshot

Leveraging the Power of Zero-Shot Learning for Malware Detection Using Application Programming Interface Call Sequences

inferred← citeshttps://doi.org/10.1109/acit62805.2024.10876988openalex-snapshot

Unveiling Android Malware: A Deep Learning Perspective Using RNN and LSTM

inferred← citeshttps://doi.org/10.1109/aic61668.2024.10730900openalex-snapshot

Multi-feature image analysis for Android malware classification using convolutional neural networks

inferred← citeshttps://doi.org/10.1109/aihcir61661.2023.00072openalex-snapshot

Network Flow-Based Dynamic Detection System for Android Adware Using Pre-Trained Models

inferred← citeshttps://doi.org/10.1109/aiiot58432.2024.10574581openalex-snapshot

Dynamic Behaviour analysis and interpretation of Malware in Android devices using Ensemble Machine Learning

inferred← citeshttps://doi.org/10.1109/aiiot58432.2024.10574689openalex-snapshot

Towards Explainability Using ML And Deep Learning Models For Malware Threat Detection

inferred← citeshttps://doi.org/10.1109/aips64124.2024.00027openalex-snapshot

Android Malware Detection Method Based on Graph Convolutional Networks

inferred← citeshttps://doi.org/10.1109/aist55798.2022.10065236openalex-snapshot

Dynamic Android Malware Detection Using Light Gradient Boosting Machine

inferred← citeshttps://doi.org/10.1109/apwimob64015.2024.10792965openalex-snapshot

Optimization of Accuracy Using Ensamble Stacking to Detect Android Malware

inferred← citeshttps://doi.org/10.1109/asew52652.2021.00019openalex-snapshot

Android Malware Detection: Looking beyond Dalvik Bytecode

inferred← citeshttps://doi.org/10.1109/asiancon58793.2023.10269963openalex-snapshot

Malware Detection in Android Based Devices by a Hybrid Approach Using Machine Learning Techniques

inferred← citeshttps://doi.org/10.1109/bdai62182.2024.10692672openalex-snapshot

RoD2M: Robust Deep Multi-modal Multi-label Model for Mobile App Security Rating

inferred← citeshttps://doi.org/10.1109/bigcomp64353.2025.00051openalex-snapshot

Advancing Android Malware Detection with Deep Learning & LLMs in Big Data Ecosystem: A Forward Vision

inferred← citeshttps://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00023openalex-snapshot

Malware Detection Techniques Based on Deep Learning

inferred← citeshttps://doi.org/10.1109/c4d65382.2025.11306458openalex-snapshot

Privacy-Preserving Android Malware Detection Using Deep Federated Learning

inferred← citeshttps://doi.org/10.1109/compsac57700.2023.00083openalex-snapshot

Reconstructing Android User Behavior through Timestamped State Models

inferred← citeshttps://doi.org/10.1109/compsac69091.2026.00087openalex-snapshot

SensiDroid: A Multimodal Framework Guided by Sensitive Behavior Chains for Android Malware Detection

inferred← citeshttps://doi.org/10.1109/confluence51648.2021.9377123openalex-snapshot

Machine Learning Based Improved Malware Detection Schemes

inferred← citeshttps://doi.org/10.1109/csci54926.2021.00050openalex-snapshot

Taxonomy for Malware Detection to Enhance the Security of Smart Devices using AI

showing the first 200 of 442

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