Cover Image

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	McFadden, Shae, Foley, Myles, D'Onghia, Mario, Hicks, Chris, Mavroudis, Vasilios, Paoletti, Nicola, Pierazzi, Fabio
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
by: McFadden, Shae, et al.
Published: (2026)

Autonomous Network Defence using Reinforcement Learning
by: Foley, Myles, et al.
Published: (2024)

One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image
by: Shereen, Ezzeldin, et al.
Published: (2025)

Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
by: Chow, Theo, et al.
Published: (2025)

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

Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
by: Vyas, Sanyam, et al.
Published: (2024)

TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
by: Kan, Zeliang, et al.
Published: (2024)

Entity-based Reinforcement Learning for Autonomous Cyber Defence
by: Thompson, Isaac Symes, et al.
Published: (2024)

Less is more? Rewards in RL for Cyber Defence
by: Bates, Elizabeth, et al.
Published: (2025)

Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning
by: Vyas, Sanyam, et al.
Published: (2025)

CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents
by: Emerson, Harry, et al.
Published: (2024)

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
by: Digregorio, Gabriele, et al.
Published: (2025)

Towards Quantum-Resistant Trusted Computing: Architectures for Post-Quantum Integrity Verification Techniques
by: D'Onghia, Grazia, et al.
Published: (2026)

Zero-Trust Network Access (ZTNA)
by: Mavroudis, Vasilios
Published: (2024)

What if we could hot swap our Biometrics?
by: Crowcroft, Jon, et al.
Published: (2025)

Analysis of Publicly Accessible Operational Technology and Associated Risks
by: Rodda, Matthew, et al.
Published: (2025)

Quantifying Mix Network Privacy Erosion with Generative Models
by: Mavroudis, Vasilios, et al.
Published: (2025)

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
by: Abuadbba, Alsharif, et al.
Published: (2025)

HonestCyberEval: An AI Cyber Risk Benchmark for Automated Software Exploitation
by: Ristea, Dan, et al.
Published: (2024)

Referential Security as a New Paradigm for AI Evaluations
by: Ristea, Dan, et al.
Published: (2026)

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

Building Better Environments for Autonomous Cyber Defence
by: Hicks, Chris, et al.
Published: (2026)

Shaping a Quantum-Resistant Future: Strategies for Post-Quantum PKI
by: D'Onghia, Grazia, et al.
Published: (2026)

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

On the Effectiveness of Adversarial Training on Malware Classifiers
by: Bostani, Hamid, et al.
Published: (2024)

An Attentive Graph Agent for Topology-Adaptive Cyber Defence
by: Sandoval, Ilya Orson, et al.
Published: (2025)

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

How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
by: Panebianco, Francesco, et al.
Published: (2025)

Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)

FARM: Few-shot Adaptive Malware Family Classification under Concept Drift
by: Guldemir, Numan Halit, et al.
Published: (2026)

ML-Based Behavioral Malware Detection Is Far From a Solved Problem
by: Kaya, Yigitcan, et al.
Published: (2024)

PackHero: A Scalable Graph-based Approach for Efficient Packer Identification
by: Di Gennaro, Marco, et al.
Published: (2025)

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

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
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)

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

Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2026)

Coding Malware in Fancy Programming Languages for Fun and Profit
by: Apostolopoulos, Theodoros, et al.
Published: (2025)

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
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)