Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Adrian Shuai, Iyengar, Arun, Kundu, Ashish, Bertino, Elisa |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Extended Dataset)
by: Li, Adrian Shuai, et al.
Published: (2025)
by: Li, Adrian Shuai, et al.
Published: (2025)
LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection
by: Li, Adrian Shuai, et al.
Published: (2025)
by: Li, Adrian Shuai, et al.
Published: (2025)
LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models
by: Akil, Md Ajwad, et al.
Published: (2025)
by: Akil, Md Ajwad, et al.
Published: (2025)
Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats
by: Li, Adrian Shuai, et al.
Published: (2026)
by: Li, Adrian Shuai, et al.
Published: (2026)
Transfer Learning for Security: Challenges and Future Directions
by: Li, Adrian Shuai, et al.
Published: (2024)
by: Li, Adrian Shuai, et al.
Published: (2024)
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
by: Barbero, Federico, et al.
Published: (2020)
by: Barbero, Federico, et al.
Published: (2020)
Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)
by: Mishra, Aniket, et al.
Published: (2025)
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
by: He, Yiling, et al.
Published: (2024)
by: He, Yiling, et al.
Published: (2024)
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
by: Roh, Eunjin, et al.
Published: (2025)
by: Roh, Eunjin, et al.
Published: (2025)
Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)
by: Sabbah, Ahmed, et al.
Published: (2025)
Counteracting Concept Drift by Learning with Future Malware Predictions
by: Bosansky, Branislav, et al.
Published: (2024)
by: Bosansky, Branislav, et al.
Published: (2024)
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)
by: McFadden, Shae, et al.
Published: (2025)
Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2026)
by: Sabbah, Ahmed, et al.
Published: (2026)
ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection
by: Alam, Md Tanvirul, et al.
Published: (2025)
by: Alam, Md Tanvirul, et al.
Published: (2025)
Optimized Deep Learning Models for Malware Detection under Concept Drift
by: Maillet, William, et al.
Published: (2023)
by: Maillet, William, et al.
Published: (2023)
Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)
by: Sabbah, Ahmed, et al.
Published: (2025)
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
by: Haque, Md Ahsanul, et al.
Published: (2025)
by: Haque, Md Ahsanul, et al.
Published: (2025)
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
by: Kalný, Tomáš, et al.
Published: (2026)
by: Kalný, Tomáš, et al.
Published: (2026)
SoK: Leveraging Transformers for Malware Analysis
by: Kunwar, Pradip, et al.
Published: (2024)
by: Kunwar, Pradip, et al.
Published: (2024)
Detecting and Explaining Malware Family Evolution Using Rule-Based Drift Analysis
by: Jurečková, Olha, et al.
Published: (2026)
by: Jurečková, Olha, et al.
Published: (2026)
FARM: Few-shot Adaptive Malware Family Classification under Concept Drift
by: Guldemir, Numan Halit, et al.
Published: (2026)
by: Guldemir, Numan Halit, et al.
Published: (2026)
Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
by: Acharya, Pawan, et al.
Published: (2026)
by: Acharya, Pawan, et al.
Published: (2026)
Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2026)
by: Sabbah, Ahmed, 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)
by: Haque, Md Ahsanul, et al.
Published: (2025)
McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
by: Kamol, Md Mahmuduzzaman, et al.
Published: (2026)
by: Kamol, Md Mahmuduzzaman, et al.
Published: (2026)
Semantic Data Representation for Explainable Windows Malware Detection Models
by: Švec, Peter, et al.
Published: (2024)
by: Švec, Peter, et al.
Published: (2024)
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling
by: Gond, Bishwajit Prasad, et al.
Published: (2025)
by: Gond, Bishwajit Prasad, et al.
Published: (2025)
SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
by: Amalapuram, Suresh Kumar, et al.
Published: (2026)
by: Amalapuram, Suresh Kumar, et al.
Published: (2026)
Using Retriever Augmented Large Language Models for Attack Graph Generation
by: Prapty, Renascence Tarafder, et al.
Published: (2024)
by: Prapty, Renascence Tarafder, et al.
Published: (2024)
Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluation
by: Rondanini, Christian, et al.
Published: (2025)
by: Rondanini, Christian, et al.
Published: (2025)
LLM-Generated Samples for Android Malware Detection
by: Rollinson, Nik, et al.
Published: (2025)
by: Rollinson, Nik, et al.
Published: (2025)
Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
by: Ponte, Andrea, et al.
Published: (2025)
by: Ponte, Andrea, et al.
Published: (2025)
Mitigating the Impact of Malware Evolution on API Sequence-based Windows Malware Detector
by: Wei, Xingyuan, et al.
Published: (2024)
by: Wei, Xingyuan, et al.
Published: (2024)
secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python
by: Demetrio, Luca, et al.
Published: (2021)
by: Demetrio, Luca, et al.
Published: (2021)
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
by: Gibert, Daniel
Published: (2024)
by: Gibert, Daniel
Published: (2024)
EarlyMalDetect: A Novel Approach for Early Windows Malware Detection Based on Sequences of API Calls
by: Maniriho, Pascal, et al.
Published: (2024)
by: Maniriho, Pascal, et al.
Published: (2024)
Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples
by: Kozak, Matous, et al.
Published: (2024)
by: Kozak, Matous, et al.
Published: (2024)
MalwarePT: A Binary-Level Foundation Model for Malware Analysis
by: Vasan, Saastha, et al.
Published: (2026)
by: Vasan, Saastha, et al.
Published: (2026)
On the Robustness of Malware Detectors to Adversarial Samples
by: Salman, Muhammad, et al.
Published: (2024)
by: Salman, Muhammad, et al.
Published: (2024)
Deep Learning Models for Detecting Malware Attacks
by: Maniriho, Pascal, et al.
Published: (2022)
by: Maniriho, Pascal, et al.
Published: (2022)
Similar Items
-
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Extended Dataset)
by: Li, Adrian Shuai, et al.
Published: (2025) -
LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection
by: Li, Adrian Shuai, et al.
Published: (2025) -
LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models
by: Akil, Md Ajwad, et al.
Published: (2025) -
Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats
by: Li, Adrian Shuai, et al.
Published: (2026) -
Transfer Learning for Security: Challenges and Future Directions
by: Li, Adrian Shuai, et al.
Published: (2024)