paper-with-me

홈 › Papers

RefinementEngine: Automating Intent-to-Device Filtering Policy Deployment under Network Constraints

2026-04-02 · Davide Colaiacomo, Chiara Bonfanti, Cataldo Basile arxiv

Translating security intent into deployable network enforcement rules and maintaining their effectiveness despite evolving cyber threats remains a largely manual process in most Security Operations Centers (SOCs). In large and heterogeneous networks, this challenge is complicated by topology-dependent reachability constraints and device-specific security control capabilities, making the process slow, error-prone, and a recurring source of misconfigurations. This paper presents RefinementEngine, an engine that automates the refinement of high-level security intents into low-level, deployment-ready configurations. Given a network topology, devices, and available security controls, along with high-level intents and Cyber Threat Intelligence (CTI) reports, RefinementEngine automatically generates settings that implement the desired intent, counter reported threats, and can be directly deployed on target security controls. The proposed approach is validated through real-world use cases on packet and web filtering policies derived from actual CTI reports, demonstrating both correctness, practical applicability, and adaptability to new data.

📄 PDF Abstract BibTeX arXiv:2604.01627

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Darknet Traffic Big-Data Analysis and Network Management to Real-Time Automating the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

2021-02-16 · Konstantinos Demertzis, Konstantinos Tsiknas, Dimitrios Takezis, Charalabos Skianis 외

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to p…

Cloud ComputingIntent DetectionManagement

S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks

2024-07-23 · Neha A S, Vivek Chaturvedi, Muhammad Shafique

Vision Transformer (ViT) is becoming widely popular in automating accurate disease diagnosis in medical imaging owing to its robust self-attention mechanism. However, ViTs remain vulnerable to adversarial attacks that ma…

image-classificationImage ClassificationImage EnhancementNVIDIA Jetson Orin Nano

From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation

2025-08-13 · Ke Niu, Haiyang Yu, Zhuofan Chen, Mengyang Zhao 외 arxiv

Computer-Aided Design (CAD) plays a vital role in engineering and manufacturing, yet current CAD workflows require extensive domain expertise and manual modeling effort. Recent advances in large language models (LLMs) ha…

Reinforcement LearningLogical ReasoningCode Generation

Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation

2021-05-20 · ICML Workshop AutoML 2021 7 · Maxime Heuillet, Benoit Debaque, Audrey Durand

The goal of Automated Machine Learning (AutoML) is to make Machine Learning (ML) tools more accessible. Collaborative Filtering (CF) methods have shown great success in automating the creation of machine learning pipelin…

AutoMLBIG-bench Machine LearningCollaborative Filtering

Iron: Intent-Aligned and Retrospective Dual Learning Framework for Enhancing Generalist Virtual Agents

2026-08-28 · Jiahe Ying, Wendong Bu, Kaihang Pan, Bingchen Miao 외 arxiv

Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal Large Language Models (MLLMs) offer enhanced visual perception and rea…