Firewall Regulatory Networks for Autonomous Cyber Defense
- URL: http://arxiv.org/abs/2505.01436v1
- Date: Thu, 24 Apr 2025 00:43:10 GMT
- Title: Firewall Regulatory Networks for Autonomous Cyber Defense
- Authors: Qi Duan, Ehab Al-Shaer,
- Abstract summary: We present the principles of designing new self-organising and autonomous management protocol to govern the dynamics of bio-inspired decentralized firewall architecture.<n> Firewall Regulatory Networks (FRN) exhibits the following features: automatic rule policy configuration with provable utility-risk appetite guarantee, resilient response for changing risks or new service requirements, and globally optimized access control policy reconciliation.
- Score: 4.297070083645049
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In this paper, we present the principles of designing new self-organising and autonomous management protocol to govern the dynamics of bio-inspired decentralized firewall architecture based on Biological Regularity Networks. The new architecture called Firewall Regulatory Networks (FRN) exhibits the following features (1) automatic rule policy configuration with provable utility-risk appetite guarantee, (2) resilient response for changing risks or new service requirements, and (3) globally optimized access control policy reconciliation. We present the FRN protocol and formalize the constraints to synthesize the undetermined components in the protocol to produce interactions that can achieve these objectives. We illustrate the feasibility of the FRN architecture in multiple case studies.
Related papers
- AI-Driven Dynamic Firewall Optimization Using Reinforcement Learning for Anomaly Detection and Prevention [0.0]
This paper proposes a novel AI-driven dynamic firewall optimization framework.<n>It autonomously adapts and updates firewall rules in response to evolving network threats.<n>Results demonstrate significant improvements in detection accuracy, false positive reduction, and rule update latency.
arXiv Detail & Related papers (2025-05-21T17:05:33Z) - The Unified Control Framework: Establishing a Common Foundation for Enterprise AI Governance, Risk Management and Regulatory Compliance [0.6554326244334866]
We propose a comprehensive governance approach that integrates risk management and regulatory compliance through a unified set of controls.<n>The UCF consists of three key components: a comprehensive risk taxonomy, structured policy requirements, and a parsimonious set of 42 controls.<n>We validate the UCF by mapping it to the Colorado AI Act, demonstrating how our approach enables efficient, adaptable governance.
arXiv Detail & Related papers (2025-03-07T21:14:49Z) - Building Hybrid B-Spline And Neural Network Operators [0.0]
Control systems are indispensable for ensuring the safety of cyber-physical systems (CPS)
We propose a novel strategy that combines the inductive bias of B-splines with data-driven neural networks to facilitate real-time predictions of CPS behavior.
arXiv Detail & Related papers (2024-06-06T21:54:59Z) - Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization: Technical Report [1.4201040196058878]
This work proposes Online Deep Reinforcement Learning-based Controls (ODRLC) as an alternative to traditional Deep Reinforcement Learning (DRL) methods.
ODRLC uses online interactions to learn optimal control policies for queuing networks (SQNs)
We introduce a method to design these intervention-assisted policies to ensure strong stability of the network.
arXiv Detail & Related papers (2024-04-05T14:02:04Z) - What Planning Problems Can A Relational Neural Network Solve? [91.53684831950612]
We present a circuit complexity analysis for relational neural networks representing policies for planning problems.
We show that there are three general classes of planning problems, in terms of the growth of circuit width and depth.
We also illustrate the utility of this analysis for designing neural networks for policy learning.
arXiv Detail & Related papers (2023-12-06T18:47:28Z) - A State-Augmented Approach for Learning Optimal Resource Management
Decisions in Wireless Networks [58.720142291102135]
We consider a radio resource management (RRM) problem in a multi-user wireless network.
The goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users.
We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints.
arXiv Detail & Related papers (2022-10-28T21:24:13Z) - Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications.
We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology.
We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
arXiv Detail & Related papers (2021-06-03T16:45:40Z) - Decentralized Control with Graph Neural Networks [147.84766857793247]
We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers.
GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties.
The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
arXiv Detail & Related papers (2020-12-29T18:59:14Z) - Phase Configuration Learning in Wireless Networks with Multiple
Reconfigurable Intelligent Surfaces [50.622375361505824]
Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation.
One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs.
We devise low-complexity supervised learning approaches for the RISs' phase configurations.
arXiv Detail & Related papers (2020-10-09T05:35:27Z) - Global Robustness Verification Networks [33.52550848953545]
We develop a global robustness verification framework with three components.
New network architecture Sliding Door Network (SDN) enabling feasible rule-based back-propagation''
We demonstrate the effectiveness of our approach on both synthetic and real datasets.
arXiv Detail & Related papers (2020-06-08T08:09:20Z) - RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment
and Passive Beamforming Design [116.88396201197533]
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS)
The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency.
A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset.
A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
arXiv Detail & Related papers (2020-01-28T14:37:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.