Automated Firewall Configuration in Virtual Networks
By Daniele Bringhenti, Guido Marchetto, Riccardo Sisto, R. Sisto, Fulvio Valenza, Jalolliddin Yusupov. 2023
430 citations recorded by the source corpus.
The configuration of security functions in computer networks is still typically performed manually, which likely leads to security breaches and long re-configuration times. This problem is exacerbated for modern networks based on network virtualization, because their complexity and dynamics make a correct manual configuration practically unfeasible. This article focuses on packet filters, i.e., the most common firewall technology used in computer networks, and it proposes a new methodology to automatically define the allocation scheme and configuration of packet filters in the logical topology of a virtual network. The proposed method is based on solving a carefully designed partial weighted Maximum Satisfiability Modulo Theories problem by means of a state-of-the-art solver. This approach formally guarantees the correctness of the solution, i.e., that all security requirements are satisfied, and it minimizes the number of needed firewalls and firewall rules. This methodology is extensively evaluated using different metrics and tests on both synthetic and real use cases, and compared to the state-of-the-art solutions, showing its superiority.
Subfields: Computer Networks and Communications, Hardware and Architecture, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Hardware and Architecture
In Natural Language Processing
- Yejin Choi
- Sarah Wiegreffe
- Swabha Swayamdipta
- Jack Hessel
- Mark O. Riedl
- Ana Marasovic
- Mohit Bansal
- Noah A. Smith
Citations in the corpus
- Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021, background): “(yes or no) This question is designed to test for clarity, aligning with characteristics such as coherence (Lei et al., 2016) and human-likeness and understandability (Ehsan et al., 2019).”
- Dense reinforcement learning for safety validation of autonomous vehicles (2023, background): “Here a crash is defined as a contact that the subject vehicle (for example, AV) has with an object, either moving or fixed, at any speed resulting in fatality, injury or property damage 39 .”