Cover Image

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rigaki, Maria, Garcia, Sebastian
Format:	Preprint
Published:	2023
Subjects:	Cryptography and Security Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments
by: Rigaki, Maria, et al.
Published: (2023)

Creating Valid Adversarial Examples of Malware
by: Kozák, Matouš, et al.
Published: (2023)

A Novel Reinforcement Learning Model for Post-Incident Malware Investigations
by: Dunsin, Dipo, et al.
Published: (2024)

Adversarial Attacks on Transformers-Based Malware Detectors
by: Jakhotiya, Yash, et al.
Published: (2022)

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
by: Feng, Chao, et al.
Published: (2023)

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
by: Gibert, Daniel, et al.
Published: (2024)

LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors
by: Lan, Tianwei, et al.
Published: (2025)

AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections
by: Keshava, Rakesh, et al.
Published: (2026)

A Unified Evaluation of Learning-Based Similarity Techniques for Malware Detection
by: Prasad, Udbhav, et al.
Published: (2026)

Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
by: Ling, Xiang, et al.
Published: (2021)

Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
by: Gibert, Daniel, et al.
Published: (2023)

A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
by: Gibert, Daniel, et al.
Published: (2024)

Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
by: Gibert, Daniel, et al.
Published: (2024)

Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
by: Stalin, Kawana, et al.
Published: (2024)

Learning Temporal Invariance in Android Malware Detectors
by: Zheng, Xinran, et al.
Published: (2025)

Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments
by: Rigaki, Maria, et al.
Published: (2024)

Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
by: Acharya, Pawan, et al.
Published: (2026)

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
by: Etter, Brian, et al.
Published: (2024)

Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges
by: Darwish, Rami, et al.
Published: (2024)

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem
by: Hitaj, Dorjan, et al.
Published: (2024)

Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning
by: Gilkarov, Daniel, et al.
Published: (2024)

Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
by: Domico, Kyle, et al.
Published: (2025)

Obfuscated Memory Malware Detection
by: P, Sharmila S, et al.
Published: (2024)

Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response
by: Dunsin, Dipo, et al.
Published: (2024)

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
by: Abedin, Md Min-Ha-Zul, et al.
Published: (2025)

Malware Detection Through Memory Analysis
by: Nassar, Sarah
Published: (2026)

Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware
by: Walton, Brandon J, et al.
Published: (2025)

Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection
by: Shokouhinejad, Hossein, et al.
Published: (2026)

A Decompilation-Driven Framework for Malware Detection with Large Language Models
by: Chawla, Aniesh, et al.
Published: (2026)

Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
by: Gibert, Daniel
Published: (2024)

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

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
by: Eghtesad, Taha, et al.
Published: (2026)

LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution
by: Pohlenz, Christopher G. Pedraza, et al.
Published: (2026)

Empirical Quantification of Spurious Correlations in Malware Detection
by: Perasso, Bianca, et al.
Published: (2025)

Semantic Preprocessing for LLM-based Malware Analysis
by: Marais, Benjamin, et al.
Published: (2025)

Leveraging LSTM and GAN for Modern Malware Detection
by: Gupta, Ishita, et al.
Published: (2024)

AdaDoS: Adaptive DoS Attack via Deep Adversarial Reinforcement Learning in SDN
by: Shao, Wei, et al.
Published: (2025)

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
by: He, Ping, et al.
Published: (2025)

MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification
by: Park, Jimin, et al.
Published: (2025)