Cover Image

NASimJax: GPU-Accelerated Policy Learning Framework for Penetration Testing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Simon, Raphael, Carrasquel, José, Mees, Wim, Libin, Pieter
Format:	Preprint
Published:	2026
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

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
by: Simon, Raphael, et al.
Published: (2025)

Spin: An Efficient Secure Computation Framework with GPU Acceleration
by: Jiang, Wuxuan, et al.
Published: (2024)

Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning
by: Zhou, Shicheng, et al.
Published: (2024)

GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks
by: Li, Zihao, et al.
Published: (2026)

Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees
by: Nakano, Katsuaki, et al.
Published: (2025)

Leveraging AI to optimize website structure discovery during Penetration Testing
by: Antonelli, Diego, et al.
Published: (2021)

Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine
by: Li, Yuanliang, et al.
Published: (2024)

Quantifying Policy Administration Cost in an Active Learning Framework
by: Zhang, Si, et al.
Published: (2023)

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

Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries
by: Çolhak, Furkan, et al.
Published: (2025)

CNN architecture extraction on edge GPU
by: Horvath, Peter, et al.
Published: (2024)

GenAI Security: Outsmarting the Bots with a Proactive Testing Framework
by: Bahadur, Sunil Kumar Jang, et al.
Published: (2025)

A System for Interactive Examination of Learned Security Policies
by: Hammar, Kim, et al.
Published: (2022)

CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers
by: Chen, Zhi, et al.
Published: (2026)

Exploring AI-Enabled Cybersecurity Frameworks: Deep-Learning Techniques, GPU Support, and Future Enhancements
by: Becher, Tobias, et al.
Published: (2024)

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

New Machine Learning Approaches for Intrusion Detection in ADS-B
by: Ngamboé, Mikaëla, et al.
Published: (2025)

Neutral Agent-based Adversarial Policy Learning against Deep Reinforcement Learning in Multi-party Open Systems
by: Peng, Qizhou, et al.
Published: (2025)

CECILIA: Comprehensive Secure Machine Learning Framework
by: Ünal, Ali Burak, et al.
Published: (2022)

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
by: Cundy, Chris, et al.
Published: (2020)

A Geometric Framework for Adversarial Vulnerability in Machine Learning
by: Bell, Brian
Published: (2024)

SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
by: Tavallaie, Omid, et al.
Published: (2024)

Machine Learning Driven Smishing Detection Framework for Mobile Security
by: Goel, Diksha, et al.
Published: (2024)

Secure Supervised Learning-Based Smart Home Authentication Framework
by: Sudha, K. Swapna, et al.
Published: (2024)

A Generic Machine Learning Framework for Radio Frequency Fingerprinting
by: Hiles, Alex, et al.
Published: (2025)

An Efficient Privacy-aware Split Learning Framework for Satellite Communications
by: Sun, Jianfei, et al.
Published: (2024)

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
by: Kalra, Akansha, et al.
Published: (2025)

Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning
by: Alsulaimawi, Zahir
Published: (2024)

A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)

LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
by: Kumar, Abhinav, et al.
Published: (2025)

F-RBA: A Federated Learning-based Framework for Risk-based Authentication
by: Fereidouni, Hamidreza, et al.
Published: (2024)

Differentially Private Conditional Independence Testing
by: Kalemaj, Iden, et al.
Published: (2023)

Machine Learning Models Have a Supply Chain Problem
by: Meiklejohn, Sarah, et al.
Published: (2025)

A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers
by: Dani, Jimmy, et al.
Published: (2024)

PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework
by: Commey, Daniel, et al.
Published: (2025)

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models
by: Ahmadi, Kasra, et al.
Published: (2025)

GNN-Based Code Annotation Logic for Establishing Security Boundaries in C Code
by: Gadey, Varun, et al.
Published: (2024)

Towards Harnessing the Power of LLMs for ABAC Policy Mining
by: Babasaheb, More Aayush, et al.
Published: (2025)

Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators
by: Banerjee, Sarbartha, et al.
Published: (2024)