Cover Image

Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sabbah, Ahmed, Kharma, Mohammad, Alkhanafseh, Mohammad, Jarrar, Radi, Zein, Samer, Mohaisen, David
Format:	Preprint
Published:	2026
Subjects:	Cryptography and Security Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2026)

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)

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

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
by: Kharma, Mohammed, et al.
Published: (2026)

Enhancing Reliability in LLM-Based Secure Code Generation
by: Kharma, Mohammed F., et al.
Published: (2026)

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
by: Kharma, Mohammed, et al.
Published: (2026)

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

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
by: Jaffal, Niveen O., et al.
Published: (2025)

LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
by: Haque, Md Ahsanul, et al.
Published: (2025)

Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples
by: Li, Adrian Shuai, et al.
Published: (2024)

Counteracting Concept Drift by Learning with Future Malware Predictions
by: Bosansky, Branislav, et al.
Published: (2024)

Optimized Deep Learning Models for Malware Detection under Concept Drift
by: Maillet, William, et al.
Published: (2023)

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

McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
by: Kamol, Md Mahmuduzzaman, et al.
Published: (2026)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

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

CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift
by: Haque, Md Ahsanul, et al.
Published: (2025)

Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
by: Stalin, Kawana, et al.
Published: (2024)

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

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
by: Roh, Eunjin, et al.
Published: (2025)

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

Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)

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

IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
by: Yumlembam, Rahul, et al.
Published: (2025)

LAMD: Context-driven Android Malware Detection and Classification with LLMs
by: Qian, Xingzhi, et al.
Published: (2025)

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

Through the Looking Glass: LLM-Based Analysis of AR/VR Android Applications Privacy Policies
by: Alghamdi, Abdulaziz, et al.
Published: (2025)

LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection
by: Li, Adrian Shuai, et al.
Published: (2025)

Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
by: Gond, Bishwajit Prasad, et al.
Published: (2025)

Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware
by: Walton, Brandon J, et al.
Published: (2025)

LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors
by: Lan, Tianwei, et al.
Published: (2025)

Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis
by: Kharma, Mohammed, et al.
Published: (2025)

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

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

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

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

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
by: Kalný, Tomáš, et al.
Published: (2026)

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

Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
by: Barbero, Federico, et al.
Published: (2020)

The Impact of Train-Test Leakage on Machine Learning-based Android Malware Detection
by: Liu, Guojun, et al.
Published: (2024)