Cover Image

The Impact of Train-Test Leakage on Machine Learning-based Android Malware Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Guojun, Caragea, Doina, Ou, Xinming, Roy, Sankardas
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Benchmarking Android Malware Detection: Traditional vs. Deep Learning Models
by: Liu, Guojun, et al.
Published: (2025)

Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)

Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020
by: Duarte, Diego Ferreira, et al.
Published: (2026)

Enhancing Android Malware Detection with Retrieval-Augmented Generation
by: S., Saraga, et al.
Published: (2025)

Regression-aware Continual Learning for Android Malware Detection
by: Ghiani, Daniele, et al.
Published: (2025)

Measuring and Explaining the Effects of Android App Transformations in Online Malware Detection
by: Meng, Guozhu, et al.
Published: (2025)

Android Malware Detection: A Machine Leaning Approach
by: Abdulla, Hasan
Published: (2025)

LLM-Generated Samples for Android Malware Detection
by: Rollinson, Nik, et al.
Published: (2025)

R+R: Revisiting Static Feature-Based Android Malware Detection using Machine Learning
by: Alam, Md Tanvirul, et al.
Published: (2024)

Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
by: Lan, Tianwei, et al.
Published: (2025)

Multi-label Classification for Android Malware Based on Active Learning
by: Qiao, Qijing, et al.
Published: (2024)

EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
by: Bostani, Hamid, et al.
Published: (2021)

Learning Temporal Invariance in Android Malware Detectors
by: Zheng, Xinran, et al.
Published: (2025)

Explainable Android Malware Detection and Malicious Code Localization Using Graph Attention
by: Ipek, Merve Cigdem, et al.
Published: (2025)

XAI and Android Malware Models
by: Kulkarni, Maithili, et al.
Published: (2024)

Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
by: Fu, Annan, et al.
Published: (2026)

MARD: A Multi-Agent Framework for Robust Android Malware Detection
by: Zeng, Xueying, et al.
Published: (2026)

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)

Feature-Centric Approaches to Android Malware Analysis: A Survey
by: Maganur, Shama, et al.
Published: (2025)

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
by: Bostani, Hamid, et al.
Published: (2024)

Android Malware Detection Based on RGB Images and Multi-feature Fusion
by: Wang, Zhiqiang, et al.
Published: (2024)

Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
by: He, Yiling, et al.
Published: (2024)

Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid
by: Chaieb, Meryam, et al.
Published: (2024)

BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
by: Ranaweera, Nishavi, et al.
Published: (2025)

FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection
by: Song, Shiwen, et al.
Published: (2025)

MASKDROID: Robust Android Malware Detection with Masked Graph Representations
by: Zheng, Jingnan, et al.
Published: (2024)

Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification
by: Costa, Francisco, et al.
Published: (2025)

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
by: Trung, Doan Minh, et al.
Published: (2025)

On Benchmarking Code LLMs for Android Malware Analysis
by: He, Yiling, et al.
Published: (2025)

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
by: He, Ping, et al.
Published: (2025)

Detecting Android Malware by Visualizing App Behaviors from Multiple Complementary Views
by: Meng, Zhaoyi, et al.
Published: (2024)

Enhancing Android Malware Detection: The Influence of ChatGPT on Decision-centric Task
by: Li, Yao, et al.
Published: (2024)

DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
by: Sun, Tiezhu, et al.
Published: (2024)

Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
by: Gibert, Daniel, et al.
Published: (2024)

AndroWasm: an Empirical Study on Android Malware Obfuscation through WebAssembly
by: Soi, Diego, et al.
Published: (2026)

ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research
by: Bai, Hongpeng, et al.
Published: (2025)

Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
by: Chow, Theo, et al.
Published: (2025)

Can you See me? On the Visibility of NOPs against Android Malware Detectors
by: Soi, Diego, et al.
Published: (2023)

DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
by: Daoudi, Nadia, et al.
Published: (2021)

Adversarial Patterns: Building Robust Android Malware Classifiers
by: Bhusal, Dipkamal, et al.
Published: (2022)