Malware Detection

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

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McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
May 7, 2026Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi +5Concept Drift Android Malware Detection
Department of Computer Science, University of Texas at El Paso · CrowdStrike
TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification
May 7, 2026Parthajit Borah, Upasana Sarmah, D. K. Bhattacharyya +1Malware Classification Malware Analysis
Department of Computer Science and Engineering, Tezpur University, Tezpur, 784028, Assam, India. · Computer Science, College of Engineering and Applied Science, University of Colorado,Colorado Springs, CO 80933-7150, Colorado, USA.
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
May 6, 2026Jan Dolejš, Martin Jureček, Róbert LórenczAdversarial Robustness Malware Analysis
Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, 16000, Czech Republic
Trident: Improving Malware Detection with LLMs and Behavioral Features
Apr 30, 2026Rebecca Saul, Jingzhi Jiang, Elliott Chia +1Malware Classification LLMs for Cybersecurity
University of California, Berkeley Berkeley, California, USA
eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem
Apr 29, 2026Sk Tanzir Mehedi, Raja Jurdak, Chadni Islam +2Malware Classification Software Supply Chain Security
Queensland University of Technology · Edith Cowan University · Research Graduate School, BUBT
Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
Apr 24, 2026Annan Fu, Hao Pei, Maryam TanhaConcept Drift Android Malware Detection
Khoury College of Computer Sciences Northeastern University Vancouver, Canada
Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
Apr 24, 2026Lukáš Hrdonka, Martin JurečekAdversarial Attacks Malware Classification
Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, Czech Republic
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
Apr 24, 2026Tomáš Kalný, Martin Jureček, Mark StampConcept Drift Malware Classification
Faculty of Information Technology Czech Technical University, Thakurova 9, Prague, Czech · Department of Computer Science, San Jose State University, San Jose, CA, USA
Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Apr 24, 2026Olha Jurečková, Martin Jureček, Matouš Kozák +1Adversarial Attacks Adversarial Robustness
Faculty of Information Technology, Czech Technical University in Prague, Th´akurova 9, Prague, 16000, Czech Republic
Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Apr 23, 2026Pawan Acharya, Lan ZhangConcept Drift Adversarial Attacks
Northern Arizona University, Flagstaff, AZ, USA
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
Apr 22, 2026Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2Randomized Smoothing Adversarial Attacks
Department of Computer Applications, Cochin University of Science and Technology, India · Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Adolfo Ferrata 5, 27100, Pavia, Italy
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
Mar 14, 2025Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam +2Android Malware Detection Malware Classification
1Université Paris Cité, France · University of Genova, Italy · 3INRIA, France +1
TIF: Learning Temporal Invariance in Android Malware Detectors
Feb 7, 2025Xinran Zheng, Shuo Yang, Edith C. H. Ngai +2Concept Drift Android Malware Detection
University College London, London, UK · The University of Hong Kong, Hong Kong SAR, China · Columbia University, New York, USA

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

Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi, Emilia Rivas+4

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

Concept Drift Android Malware Detection Malware Classification Malware Analysis Multimodal Learning Malware Detection

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Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying costs and benefits. Malware naturally exhibits all of these complexities, but for the same reason, it is challenging to curate and organize data to study these factors. We present McNdroid, to our knowledge the largest longitudinal multimodal Android malware benchmark for malware detection and drift analysis. McNdroid spans 2013--2025, excluding 2015, and represents each application with three aligned modalities--static features from manifests and smali code, dynamic behavioral features from sandbox execution, and graph-based features from function-call graphs. Using temporally separated splits, we evaluate standard ML and deep-learning detectors across increasing train--test time gaps. Results show clear temporal degradation, while multimodal fusion outperforms the best single modality across long-term temporal gaps. Cross-modal agreement also declines over time, suggesting that drift affects both individual feature spaces and the consistency among modalities. We further analyze modality-specific drift, malware-family evolution, and temporal changes in model explanations. We publicly release McNdroid, benchmark splits, and code to support reproducible research on temporal generalization and robust multimodal learning in security-critical, non-stationary settings.

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