Cover Image

A Comprehensive Review of Adversarial Attacks on Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ahmed, Syed Quiser, Ganesh, Bharathi Vokkaliga, Kumar, Sathyanarayana Sampath, Mishra, Prakhar, Anand, Ravi, Akurathi, Bhanuteja
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards More Realistic Extraction Attacks: An Adversarial Perspective
by: More, Yash, et al.
Published: (2024)

The Data Minimization Principle in Machine Learning
by: Ganesh, Prakhar, et al.
Published: (2024)

Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning
by: Hasan, Syed Mhamudul, et al.
Published: (2024)

SoK: Data Minimization in Machine Learning
by: Staab, Robin, et al.
Published: (2025)

A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution
by: Rajapaksha, Sampath, et al.
Published: (2024)

BlackboxBench: A Comprehensive Benchmark of Black-box Adversarial Attacks
by: Zheng, Meixi, et al.
Published: (2023)

Fingerprinting of Machines in Critical Systems for Integrity Monitoring and Verification
by: Paliwal, Prakhar, et al.
Published: (2024)

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

Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence
by: Obadinma, Stephen, et al.
Published: (2024)

A Comprehensive Analysis of Machine Learning Based File Trap Selection Methods to Detect Crypto Ransomware
by: Putrevu, Mohan Anand, et al.
Published: (2024)

A Comprehensive Review of Denial of Wallet Attacks in Serverless Architectures
by: Dorsett, Mark, et al.
Published: (2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
by: Huang, Yangsibo, et al.
Published: (2024)

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
by: Chen, Jing, et al.
Published: (2025)

Cyber Warfare During Operation Sindoor: Malware Campaign Analysis and Detection Framework
by: Paliwal, Prakhar, et al.
Published: (2025)

A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures
by: Saini, Shalini, et al.
Published: (2024)

A Comprehensive Analysis of Adversarial Attacks against Spam Filters
by: Hotoğlu, Esra, et al.
Published: (2025)

Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
by: Gu, Ruizhe, et al.
Published: (2020)

TA3: Testing Against Adversarial Attacks on Machine Learning Models
by: Jin, Yuanzhe, et al.
Published: (2024)

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

Adversarial Attacks on Reinforcement Learning Agents for Command and Control
by: Dabholkar, Ahaan, et al.
Published: (2024)

Modern Hardware Security: A Review of Attacks and Countermeasures
by: Mishra, Jyotiprakash, et al.
Published: (2025)

FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats
by: Shapira, Avishag, et al.
Published: (2025)

Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey
by: Jain, Bhavuk, et al.
Published: (2026)

Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective
by: Wu, Baoyuan, et al.
Published: (2023)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)

Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review
by: Cheng, Pengzhou, et al.
Published: (2023)

Advances in Differential Privacy and Differentially Private Machine Learning
by: Das, Saswat, et al.
Published: (2024)

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey
by: Yang, Wencheng, et al.
Published: (2025)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
by: Xue, Jing, et al.
Published: (2025)

Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
by: Chang, Jung-Woo, et al.
Published: (2023)

AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset
by: Deka, Pritam, et al.
Published: (2024)

How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks
by: P., Muhammed Shafi K., et al.
Published: (2025)

Adversarial Machine Learning for Robust Password Strength Estimation
by: Jha, Pappu, et al.
Published: (2025)

SwitchPatch: Physical Adversarial Attack Strategy with Switchable Adversarial Objectives
by: Jiang, Hanrui, et al.
Published: (2025)

Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
by: Ghosh, Archisman, et al.
Published: (2025)

A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
by: Tafreshian, Benyamin, et al.
Published: (2025)

Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
by: Yang, Yulong, et al.
Published: (2023)

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

CAMH: Advancing Model Hijacking Attack in Machine Learning
by: He, Xing, et al.
Published: (2024)

Amplifying Machine Learning Attacks Through Strategic Compositions
by: Liu, Yugeng, et al.
Published: (2025)