Buchumschlag

Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Gibert, Daniel, Zizzo, Giulio, Le, Quan
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Cryptography and Security Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

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

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

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

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

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

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

Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs
von: Momcilovic, Tomas Bueno, et al.
Veröffentlicht: (2024)

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

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

Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification
von: Gibert, Daniel, et al.
Veröffentlicht: (2022)

Blue Teaming Function-Calling Agents
von: Dolcetti, Greta, et al.
Veröffentlicht: (2026)

Adversarial Machine Learning: Attacks, Defenses, and Open Challenges
von: Jha, Pranav K
Veröffentlicht: (2025)

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

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

A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systems
von: Zhang, H., et al.
Veröffentlicht: (2025)

IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
von: Yumlembam, Rahul, et al.
Veröffentlicht: (2025)

Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs
von: Momcilovic, Tomas Bueno, et al.
Veröffentlicht: (2024)

Autoencoder-based Denoising Defense against Adversarial Attacks on Object Detection
von: Song, Min Geun, et al.
Veröffentlicht: (2025)

I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectors
von: Lin, Zijin, et al.
Veröffentlicht: (2024)

Critical-CoT: A Robust Defense Framework against Reasoning-Level Backdoor Attacks in Large Language Models
von: Truong, Vu Tuan, et al.
Veröffentlicht: (2026)

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
von: Li, Xiaohu, et al.
Veröffentlicht: (2025)

A Practical Adversarial Attack against Sequence-based Deep Learning Malware Classifiers
von: Tan, Kai, et al.
Veröffentlicht: (2025)

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

A Critical Evaluation of Defenses against Prompt Injection Attacks
von: Jia, Yuqi, et al.
Veröffentlicht: (2025)

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
von: Yichao, Wu, et al.
Veröffentlicht: (2025)

Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
von: Thakkar, Janvi, et al.
Veröffentlicht: (2023)

Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
von: Belkhiter, Yannis, et al.
Veröffentlicht: (2026)

Attention Masks Help Adversarial Attacks to Bypass Safety Detectors
von: Shi, Yunfan
Veröffentlicht: (2024)

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

Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
von: Lin, Zheng, et al.
Veröffentlicht: (2026)

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

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

Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models
von: Ma, Jiachen, et al.
Veröffentlicht: (2024)

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
von: Fu, Haowei, et al.
Veröffentlicht: (2025)

MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks
von: Cornacchia, Giandomenico, et al.
Veröffentlicht: (2024)

ByteShield: Adversarially Robust End-to-End Malware Detection through Byte Masking
von: Gibert, Daniel, et al.
Veröffentlicht: (2025)

Energy-Latency Attacks: A New Adversarial Threat to Deep Learning
von: Meftah, Hanene F. Z. Brachemi, et al.
Veröffentlicht: (2025)

Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization
von: Li, Xurui, et al.
Veröffentlicht: (2025)

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
von: M., Sameera K., et al.
Veröffentlicht: (2025)

Behavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDS
von: Ennaji, Sabrine, et al.
Veröffentlicht: (2025)