Android Malware Detection

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Overview

Android malware detection classifies applications using static signals such as permissions and code structure, dynamic runtime behavior, or graphs of control and function calls. Common approaches include machine-learning classifiers, deep and self-supervised representations, multimodal fusion, and semi-supervised methods that reduce reliance on labeled apps. Time-ordered benchmarks show that changes in apps and malware families can weaken detectors, while adversarial APK modifications can evade them; drift adaptation, robust evaluation, and efficient retraining are active approaches. A central challenge is maintaining reliable detection as distributions shift and attackers adapt, without requiring costly labels or excessive device resources.

Latest papers

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16 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning

    Sep 22, 2026Han Chen, Hanchen Wang, Hongmei Chen +3Unsupervised Domain AdaptationConcept Drift

    University of Technology Sydney Sydney, New South Wales, Australia · Yunnan University Kunming, Yunnan, China · University of New South Wales Sydney, New South Wales, Australia

  2. REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

    Aug 28, 2026Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia +5Adversarial AttacksBlack-Box Adversarial Attacks

    King’s College London · The Alan Turing Institute · University College London +3

  3. Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution

    Aug 26, 2026Md Rafid Islam, Zahid Hasan, Hafiz Abdur RahmanPseudo-LabelingMalware Classification

    Department of Electrical and Computer Engineering North South University Dhaka, Bangladesh

  4. ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

    Aug 4, 2026Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil +4Android Malware DetectionMalware Classification

    Department of Computer Science and Engineering Brac University Dhaka, Bangladesh

  5. Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

    Jul 22, 2026Shrinidhi Sridhar, Vikas K. MalviyaEnergy-Efficient MLAndroid Malware Detection

    MIE-SPPU Institute of Higher Education · Doha, Qatar

  6. DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors

    Jun 25, 2026Christian Scano, Diego Soi, Angelo Sotgiu +5Adversarial AttacksAndroid Malware Detection

    University of Cagliari · Sapienza University of Rome · CINI +1

  7. MASCOT-Android: A Curated Dataset and Automated Collection Pipeline for Android Malware Source Code Specimens

    Jun 15, 2026Bojing Li, Duo Zhong, Prajna Bhandary +4Android Malware DetectionMalware Analysis

    University of Maryland, Baltimore County Baltimore, Maryland, USA

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

    May 22, 2026Ahmed Sabbah, Mohammad Kharma, Mohammad Alkhanafseh +3Reinforcement LearningConcept Drift

    Birzeit University · University of Central Florida

  9. Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

    May 22, 2026Ahmed Sabbah, Mohammed Kharma, Radi Jarrar +2Distribution Shift RobustnessConcept Drift

    aDepartment of Computer Science, Birzeit University, P.O. Box 14, Birzeit, P627, West2026 Bank, Palestine · bDepartment of Computer Science, University of Central Florida, 4328 Scorpius St., Building 116, Room 246, Orlando, 32816, FL, USAMay 22

  10. Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection

    May 9, 2026Md Rafid IslamAndroid Malware DetectionCross-Domain Generalization

    North South University, Department of Electrical and Computer Engineering, Bashundhara, Dhaka, 1229, Bangladesh

  11. McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware

    May 7, 2026Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi +5Concept DriftAndroid Malware Detection

    Department of Computer Science, University of Texas at El Paso · CrowdStrike

  12. Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset

    Apr 24, 2026Annan Fu, Hao Pei, Maryam TanhaConcept DriftAndroid Malware Detection

    Khoury College of Computer Sciences Northeastern University Vancouver, Canada

  13. Label-efficient Training Updates for Malware Detection over Time

    Mar 30, 2026Luca Minnei, Cristian Manca, Giorgio Piras +6Malware DetectionActive Learning

    Department of Electrical and Electronic Engineering, University of Cagliari, Italy · Department of Computer, Control and Management Engineering, Sapienza University, Rome, Italy · CINI, Rome, Italy

  14. Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark

    Aug 8, 2025Ngoc N. Tran, Anwar Said, Waseem Abbas +2Distribution Shift RobustnessGraph Representation Learning

    Vanderbilt University, USA · Highmark Health, USA · University of Texas at Dallas, USA

  15. Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection

    Mar 14, 2025Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam +2Android Malware DetectionMalware Classification

    1Université Paris Cité, France · University of Genova, Italy · 3INRIA, France +1

  16. TIF: Learning Temporal Invariance in Android Malware Detectors

    Feb 7, 2025Xinran Zheng, Shuo Yang, Edith C. H. Ngai +2Concept DriftAndroid Malware Detection

    University College London, London, UK · The University of Hong Kong, Hong Kong SAR, China · Columbia University, New York, USA