Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
Fuente:
arXiv
Saved in:
| Main Authors: | Barbero, Federico, Pendlebury, Feargus, Pierazzi, Fabio, Cavallaro, Lorenzo |
|---|---|
| Format: | Preprint |
| Published: |
2020
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
by: Kan, Zeliang, et al.
Published: (2024)
by: Kan, Zeliang, et al.
Published: (2024)
Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]
by: Cortellazzi, Jacopo, et al.
Published: (2019)
by: Cortellazzi, Jacopo, et al.
Published: (2019)
On the Effectiveness of Adversarial Training on Malware Classifiers
by: Bostani, Hamid, et al.
Published: (2024)
by: Bostani, Hamid, et al.
Published: (2024)
Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)
by: Liu, Jiahao, et al.
Published: (2024)
Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
by: Chow, Theo, et al.
Published: (2025)
by: Chow, Theo, et al.
Published: (2025)
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)
by: McFadden, Shae, et al.
Published: (2025)
ML-Based Behavioral Malware Detection Is Far From a Solved Problem
by: Kaya, Yigitcan, et al.
Published: (2024)
by: Kaya, Yigitcan, et al.
Published: (2024)
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples
by: Li, Adrian Shuai, et al.
Published: (2024)
by: Li, Adrian Shuai, et al.
Published: (2024)
On the Reliability and Stability of Selective Methods in Malware Classification Tasks
by: Herzog, Alexander, et al.
Published: (2025)
by: Herzog, Alexander, et al.
Published: (2025)
The Adaptive Arms Race: Redefining Robustness in AI Security
by: Tsingenopoulos, Ilias, et al.
Published: (2023)
by: Tsingenopoulos, Ilias, et al.
Published: (2023)
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)
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)
Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)
by: Mishra, Aniket, et al.
Published: (2025)
LAMD: Context-driven Android Malware Detection and Classification with LLMs
by: Qian, Xingzhi, et al.
Published: (2025)
by: Qian, Xingzhi, 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)
Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing
by: Zheng, Xinran, et al.
Published: (2025)
by: Zheng, Xinran, et al.
Published: (2025)
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
by: He, Ping, et al.
Published: (2025)
by: He, Ping, 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)
How to Train your Antivirus: RL-based Hardening through the Problem-Space
by: Tsingenopoulos, Ilias, et al.
Published: (2024)
by: Tsingenopoulos, Ilias, et al.
Published: (2024)
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)
Learning Temporal Invariance in Android Malware Detectors
by: Zheng, Xinran, et al.
Published: (2025)
by: Zheng, Xinran, et al.
Published: (2025)
Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification
by: Costa, Francisco, et al.
Published: (2025)
by: Costa, Francisco, 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)
by: Gond, Bishwajit Prasad, et al.
Published: (2025)
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)
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)
On Benchmarking Code LLMs for Android Malware Analysis
by: He, Yiling, et al.
Published: (2025)
by: He, Yiling, 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)
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)
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)
Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics
by: Li, Shuai, et al.
Published: (2024)
by: Li, Shuai, et al.
Published: (2024)
On the Security Risks of ML-based Malware Detection Systems: A Survey
by: He, Ping, et al.
Published: (2025)
by: He, Ping, et al.
Published: (2025)
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)
Malware Classification Based on Image Segmentation
by: Nie, Wanhu
Published: (2024)
by: Nie, Wanhu
Published: (2024)
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)
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)
Semantic Entanglement-Based Ransomware Detection via Probabilistic Latent Encryption Mapping
by: Eisa, Mohammad, et al.
Published: (2025)
by: Eisa, Mohammad, et al.
Published: (2025)
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
by: Guo, Xin Fan, et al.
Published: (2025)
by: Guo, Xin Fan, et al.
Published: (2025)
Multimodal Techniques for Malware Classification
by: Jiang, Jonathan, et al.
Published: (2025)
by: Jiang, Jonathan, et al.
Published: (2025)
Similar Items
-
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
by: Kan, Zeliang, et al.
Published: (2024) -
Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]
by: Cortellazzi, Jacopo, et al.
Published: (2019) -
On the Effectiveness of Adversarial Training on Malware Classifiers
by: Bostani, Hamid, et al.
Published: (2024) -
Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024) -
Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
by: Chow, Theo, et al.
Published: (2025)