Cover Image

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Etter, Brian, Hu, James Lee, Ebrahimi, Mohammedreza, Li, Weifeng, Li, Xin, Chen, Hsinchun
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AuthorMist: Evading AI Text Detectors with Reinforcement Learning
by: David, Isaac, et al.
Published: (2025)

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

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

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)

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

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
by: Gu, Hanlin, et al.
Published: (2024)

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

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

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
by: Rigaki, Maria, et al.
Published: (2023)

Cryptic Bytes: WebAssembly Obfuscation for Evading Cryptojacking Detection
by: Harnes, Håkon, et al.
Published: (2024)

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

TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors
by: Zheng, Jingyi, et al.
Published: (2025)

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

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
by: Trung, Doan Minh, et al.
Published: (2025)

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)

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

Deep Learning Models for Detecting Malware Attacks
by: Maniriho, Pascal, et al.
Published: (2022)

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
by: Arazzi, Marco, et al.
Published: (2024)

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

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

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
by: Yang, Wanrong, et al.
Published: (2024)

BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning
by: Perera, Wadduwage Shanika, et al.
Published: (2025)

Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling
by: Hu, Zhanhao, et al.
Published: (2023)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

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

Differentially Private Deep Model-Based Reinforcement Learning
by: Rio, Alexandre, et al.
Published: (2024)

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

Explainable Deep Learning Models for Dynamic and Online Malware Classification
by: Card, Quincy, et al.
Published: (2024)

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

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
by: Zhou, Ranyang, et al.
Published: (2025)

Formalization Driven LLM Prompt Jailbreaking via Reinforcement Learning
by: Wang, Zhaoqi, et al.
Published: (2025)

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage
by: Li, Na, 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)

A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild
by: Ling, Xiang, et al.
Published: (2024)

Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques
by: Fahim, Abrar, et al.
Published: (2025)

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

MEASER: Malware embedding attacks on open-source LLMs
by: Tan, Ming, et al.
Published: (2025)

Deep Learning Fusion For Effective Malware Detection: Leveraging Visual Features
by: Johny, Jahez Abraham, et al.
Published: (2024)

Benchmarking Android Malware Detection: Traditional vs. Deep Learning Models
by: Liu, Guojun, et al.
Published: (2025)