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Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware

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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/10.1007/978-3-319-11203-9_10openalex-snapshot

DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications

inferredcites →https://doi.org/10.1007/978-3-319-24177-7_15openalex-snapshot

DexHunter: Toward Extracting Hidden Code from Packed Android Applications

inferredcites →https://doi.org/10.1007/978-3-319-60876-1_12openalex-snapshot

Deep Ground Truth Analysis of Current Android Malware

inferredcites →https://doi.org/10.1007/978-3-319-66399-9_4openalex-snapshot

Adversarial Examples for Malware Detection

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/10.1088/1757-899x/336/1/012017openalex-snapshot

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

inferredcites →https://doi.org/10.1109/ase.2015.69openalex-snapshot

Static Analysis of Implicit Control Flow: Resolving Java Reflection and Android Intents (T)

inferredcites →https://doi.org/10.1109/asonam.2018.8508284openalex-snapshot

DroidEye: Fortifying Security of Learning-Based Classifier Against Adversarial Android Malware Attacks

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.2019.00014openalex-snapshot

DroidEvolver: Self-Evolving Android Malware Detection System

inferredcites →https://doi.org/10.1109/icse.2015.50openalex-snapshot

AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context

inferredcites →https://doi.org/10.1109/icse.2017.47openalex-snapshot

Exploring API Embedding for API Usages and Applications

inferredcites →https://doi.org/10.1109/icsme.2018.00028openalex-snapshot

Improving API Caveats Accessibility by Mining API Caveats Knowledge Graph

inferredcites →https://doi.org/10.1109/ijcnn.2016.7727508openalex-snapshot

Adaptive and scalable Android malware detection through online learning

inferredcites →https://doi.org/10.1109/jiot.2019.2909745openalex-snapshot

EveDroid: Event-Aware Android Malware Detection Against Model Degrading for IoT Devices

inferredcites →https://doi.org/10.1109/msr.2017.57openalex-snapshot

Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware

inferredcites →https://doi.org/10.1109/sp.2019.00003openalex-snapshot

Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization

inferredcites →https://doi.org/10.1109/tse.2013.12openalex-snapshot

Patterns of Knowledge in API Reference Documentation

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/10.1145/1376616.1376746openalex-snapshot

Freebase

inferredcites →https://doi.org/10.1145/2382196.2382222openalex-snapshot

PScout

inferredcites →https://doi.org/10.1145/2660267.2660359openalex-snapshot

Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs

inferredcites →https://doi.org/10.1145/2818000.2818042openalex-snapshot

Grab 'n Run

inferredcites →https://doi.org/10.1145/2897845.2897860openalex-snapshot

StormDroid

inferredcites →https://doi.org/10.1145/2931037.2931044openalex-snapshot

DroidRA: taming reflection to support whole-program analysis of Android apps

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

Deep Android Malware Detection

inferredcites →https://doi.org/10.1145/3371924openalex-snapshot

Assessing and Improving Malware Detection Sustainability through App Evolution Studies

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.2014.23328openalex-snapshot

Execute This! Analyzing Unsafe and Malicious Dynamic Code Loading in Android Applications

inferredcites →https://doi.org/10.14722/ndss.2018.23296openalex-snapshot

Things You May Not Know About Android (Un)Packers: A Systematic Study based on Whole-System Emulation

inferredcites →https://doi.org/10.1609/aaai.v28i1.8870openalex-snapshot

Knowledge Graph Embedding by Translating on Hyperplanes

What points at this work

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

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TL‐GNN : Android Malware Detection Using Transfer Learning

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Detecting Obfuscated Android Malware Through Categorized Smali N-Gram Instructions and Ensemble Learning

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inferred← citeshttps://doi.org/10.1007/s10207-025-01167-0openalex-snapshot

IFDroid: Enhancing Android Malware Detection Resilience Against Concept Drift Through API Sequence Intrinsic Features

inferred← citeshttps://doi.org/10.1007/s10207-026-01295-1openalex-snapshot

AdvAttack: Adversarial attack against machine learning-based Android malware detection models

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Tri-channel visualised malicious code classification based on improved ResNet

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inferred← citeshttps://doi.org/10.1007/s10664-023-10375-yopenalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s10664-025-10747-6openalex-snapshot

Func: reducing the impact of Android framework evolution on malware detection

inferred← citeshttps://doi.org/10.1007/s11036-024-02383-zopenalex-snapshot

Few-Shot Malware Classification via Attention-Based Transductive Learning Network

inferred← citeshttps://doi.org/10.1007/s11416-024-00536-yopenalex-snapshot

Experts still needed: boosting long-term android malware detection with active learning

inferred← citeshttps://doi.org/10.1007/s11432-021-3414-7openalex-snapshot

ArgusDroid: detecting Android malware variants by mining permission-API knowledge graph

inferred← citeshttps://doi.org/10.1007/s13042-021-01393-7openalex-snapshot

Do gradient-based explanations tell anything about adversarial robustness to android malware?

inferred← citeshttps://doi.org/10.1007/s41019-022-00189-1openalex-snapshot

A Risk Estimation Mechanism for Android Apps based on Hybrid Analysis

inferred← citeshttps://doi.org/10.1007/s42524-022-0228-yopenalex-snapshot

Large-scale App privacy governance

inferred← citeshttps://doi.org/10.1016/j.cose.2021.102264openalex-snapshot

GDroid: Android malware detection and classification with graph convolutional network

inferred← citeshttps://doi.org/10.1016/j.cose.2021.102399openalex-snapshot

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

inferred← citeshttps://doi.org/10.1016/j.cose.2021.102514openalex-snapshot

FCSCNN: Feature centralized Siamese CNN-based android malware identification

inferred← citeshttps://doi.org/10.1016/j.cose.2022.102996openalex-snapshot

Towards a fair comparison and realistic evaluation framework of android malware detectors based on static analysis and machine learning

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

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MDADroid: A novel malware detection method by constructing functionality-API mapping

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Fast & Furious: Modelling Malware Detection as Evolving Data Streams

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A multi-model ensemble learning framework for imbalanced android malware detection

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124095openalex-snapshot

KFFPDet: Android malicious application detection system with assisted detection of adversarial samples

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Android malware obfuscation variants detection method based on multi-granularity opcode features

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

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DTMIC: Deep transfer learning for malware image classification

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Towards time evolved malware identification using two-head neural network

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111991openalex-snapshot

Evolving malware detection through instant dynamic graph inverse reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.pmcj.2023.101849openalex-snapshot

Efficient concept drift handling for batch android malware detection models

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

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A Systematic Overview of Android Malware Detection

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DAEMON: Dataset/Platform-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining

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Interpretable Defense Against Structural Adversarial Attacks on Android Malware Detection

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Dynamic Prototype Network based on Sample Adaptation for Few-Shot Malware Detection

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inferred← citeshttps://doi.org/10.1109/tse.2025.3599732openalex-snapshot

Wolf in Sheep’s Clothing: Shearing the Camouflage of Malicious Java Components in Maven

inferred← citeshttps://doi.org/10.1109/tse.2025.3626788openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tse.2026.3698484openalex-snapshot

TIF: Learning Temporal Invariance in Android Malware Detectors

inferred← citeshttps://doi.org/10.1109/wpmc59531.2023.10338981openalex-snapshot

Hash-Based Function Call Graph Fusion Method for IoT Malware Detection

inferred← citeshttps://doi.org/10.1111/coin.12521openalex-snapshot

Malware classification using a byte‐granularity feature based on structural entropy

inferred← citeshttps://doi.org/10.1142/s0218194025500305openalex-snapshot

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inferred← citeshttps://doi.org/10.1145/3459637.3482118openalex-snapshot

GraphEvolveDroid

inferred← citeshttps://doi.org/10.1145/3459637.3482271openalex-snapshot

Learning to Learn the Future

inferred← citeshttps://doi.org/10.1145/3460120.3485387openalex-snapshot

Structural Attack against Graph Based Android Malware Detection

inferred← citeshttps://doi.org/10.1145/3465481.3470040openalex-snapshot

Multi-View Learning for Repackaged Malware Detection

inferred← citeshttps://doi.org/10.1145/3474369.3486873openalex-snapshot

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inferred← citeshttps://doi.org/10.1145/3485447.3512151openalex-snapshot

Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit

inferred← citeshttps://doi.org/10.1145/3485832.3485916openalex-snapshot

Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?

inferred← citeshttps://doi.org/10.1145/3488932.3497763openalex-snapshot

Towards a Utopia of Dataset Sharing

inferred← citeshttps://doi.org/10.1145/3510003.3510161openalex-snapshot

Rotten apples spoil the bunch

inferred← citeshttps://doi.org/10.1145/3544968openalex-snapshot

Deep Learning for Android Malware Defenses: A Systematic Literature Review

inferred← citeshttps://doi.org/10.1145/3571847openalex-snapshot

Securing the Ethereum from Smart Ponzi Schemes: Identification Using Static Features

inferred← citeshttps://doi.org/10.1145/3576915.3623117openalex-snapshot

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

inferred← citeshttps://doi.org/10.1145/3576915.3623130openalex-snapshot

"Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security Conferences

inferred← citeshttps://doi.org/10.1145/3597503.3623320openalex-snapshot

A Comprehensive Study of Learning-based Android Malware Detectors under Challenging Environments

inferred← citeshttps://doi.org/10.1145/3597926.3598051openalex-snapshot

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

inferred← citeshttps://doi.org/10.1145/3597926.3598054openalex-snapshot

API2Vec: Learning Representations of API Sequences for Malware Detection

inferred← citeshttps://doi.org/10.1145/3603273.3635055openalex-snapshot

A Multi-modality Feature Fusion Method for Android Malware Detection

inferred← citeshttps://doi.org/10.1145/3658644.3670310openalex-snapshot

PowerPeeler: A Precise and General Dynamic Deobfuscation Method for PowerShell Scripts

inferred← citeshttps://doi.org/10.1145/3674805.3690745openalex-snapshot

DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware

inferred← citeshttps://doi.org/10.1145/3678167openalex-snapshot

Automatically Recommend Code Updates: Are We There Yet?

inferred← citeshttps://doi.org/10.1145/3678890.3678901openalex-snapshot

Mateen: Adaptive Ensemble Learning for Network Anomaly Detection

inferred← citeshttps://doi.org/10.1145/3691620.3695008openalex-snapshot

MaskDroid: Robust Android Malware Detection with Masked Graph Representations

inferred← citeshttps://doi.org/10.1145/3691620.3695280openalex-snapshot

Android Malware Family Labeling: Perspectives from the Industry

inferred← citeshttps://doi.org/10.1145/3719027.3744792openalex-snapshot

Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification

inferred← citeshttps://doi.org/10.1145/3719027.3765197openalex-snapshot

Deep Learning from Imperfectly Labeled Malware Data

inferred← citeshttps://doi.org/10.1145/3748522.3779885openalex-snapshot

APIMatch-ATT&CK: Automatically Mapping Android APIs to MITRE ATT&CK Techniques

inferred← citeshttps://doi.org/10.1145/3779208.3807494openalex-snapshot

ALPHA: Active Learning with PAC-Bayesian Theory for Android Malware Detection

inferred← citeshttps://doi.org/10.1145/3809491openalex-snapshot

Unraveling the Key of Machine Learning-based Android Malware Detection

inferred← citeshttps://doi.org/10.1145/3832233openalex-snapshot

The Illusion of Success: Learning-Based Android Malware Detectors (Replicability Study)

inferred← citeshttps://doi.org/10.1155/2021/5538841openalex-snapshot

Android Malware Detection via Graph Representation Learning

inferred← citeshttps://doi.org/10.1155/2021/6642252openalex-snapshot

FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection

inferred← citeshttps://doi.org/10.1155/2021/8896013openalex-snapshot

A Survey of Android Malware Static Detection Technology Based on Machine Learning

inferred← citeshttps://doi.org/10.1155/2021/9994588openalex-snapshot

A Hybrid Modeling of Mobile App Dynamics on Serial Causality for Malware Detection

inferred← citeshttps://doi.org/10.1155/2022/8634784openalex-snapshot

A Modified ResNeXt for Android Malware Identification and Classification

inferred← citeshttps://doi.org/10.1155/2023/6447655openalex-snapshot

Toward Detecting Malware Based on Process-Aware Behaviors

inferred← citeshttps://doi.org/10.14421/csecurity.2022.5.1.3116openalex-snapshot

ANALISIS STATIS DETEKSI MALWARE ANDROID MENGGUNAKAN ALGORITMA SUPERVISED MACHINE LEARNING

inferred← citeshttps://doi.org/10.14569/ijacsa.2021.01212104openalex-snapshot

Sustainable Android Malware Detection Scheme using Deep Learning Algorithm

inferred← citeshttps://doi.org/10.14722/ndss.2023.24830openalex-snapshot

Anomaly Detection in the Open World: Normality Shift Detection, Explanation, and Adaptation

inferred← citeshttps://doi.org/10.1631/fitee.2300453openalex-snapshot

Empowering smart city situational awareness via big mobile data

inferred← citeshttps://doi.org/10.21203/rs.3.rs-3165300/v1openalex-snapshot

Android Malware Detection using LSTM with Smali Codes

inferred← citeshttps://doi.org/10.23919/cje.2021.00.451openalex-snapshot

EAODroid: Android Malware Detection Based on Enhanced API Order

inferred← citeshttps://doi.org/10.3390/app112110244openalex-snapshot

Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions

inferred← citeshttps://doi.org/10.3390/app132212528openalex-snapshot

A Malware Detection Framework Based on Semantic Information of Behavioral Features

inferred← citeshttps://doi.org/10.3390/app132312629openalex-snapshot

Android Malware Detection Based on Hypergraph Neural Networks

inferred← citeshttps://doi.org/10.3390/electronics10222881openalex-snapshot

A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

inferred← citeshttps://doi.org/10.3390/electronics11050672openalex-snapshot

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

inferred← citeshttps://doi.org/10.3390/electronics12234817openalex-snapshot

Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks

inferred← citeshttps://doi.org/10.3390/s21165671openalex-snapshot

An Analysis of Android Malware Classification Services

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/10.4018/979-8-3373-0832-6.ch011openalex-snapshot

Detection and Analysis of AI-Generated Malicious Content

inferred← citeshttps://doi.org/10.48550/arxiv.2108.07548openalex-snapshot

HAWK: Rapid Android Malware Detection through Heterogeneous Graph Attention Networks

inferred← citeshttps://doi.org/10.52756/ijerr.2023.v34spl.009openalex-snapshot

An efficient Android malware detection method using Borutashap algorithm

inferred← citeshttps://doi.org/10.5753/errc.2021.18539openalex-snapshot

Ferramentas de extração de características para análise estática de aplicativos Android

inferred← citeshttps://doi.org/10.5753/sbseg.2023.233605openalex-snapshot

Does Asm2Vec Reduce Drift on Malware Classification?

inferred← citeshttps://doi.org/10.70675/f9b82129z4dffz465ezb079z0a85d72432d3openalex-snapshot

Profiling and Visualizing Android Malware Datasets

inferred← citeshttps://doi.org/10.7717/peerj-cs.3902openalex-snapshot

Explainable AI for malware analysis: a systematic review of benchmark datasets, traffic-oriented detection, and interpretability methods

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