VulRG: Multi-Level Explainable Vulnerability Patch Ranking for Complex Systems Using Graphs
- URL: http://arxiv.org/abs/2502.11143v1
- Date: Sun, 16 Feb 2025 14:21:52 GMT
- Title: VulRG: Multi-Level Explainable Vulnerability Patch Ranking for Complex Systems Using Graphs
- Authors: Yuning Jiang, Nay Oo, Qiaoran Meng, Hoon Wei Lim, Biplab Sikdar,
- Abstract summary: This work introduces a graph-based framework for vulnerability patch prioritization.
It integrates diverse data sources and metrics into a universally applicable model.
refined risk metrics enable detailed assessments at the component, asset, and system levels.
- Score: 20.407534993667607
- License:
- Abstract: As interconnected systems proliferate, safeguarding complex infrastructures against an escalating array of cyber threats has become an urgent challenge. The increasing number of vulnerabilities, combined with resource constraints, makes addressing every vulnerability impractical, making effective prioritization essential. However, existing risk prioritization methods often rely on expert judgment or focus solely on exploit likelihood and consequences, lacking the granularity and adaptability needed for complex systems. This work introduces a graph-based framework for vulnerability patch prioritization that optimizes security by integrating diverse data sources and metrics into a universally applicable model. Refined risk metrics enable detailed assessments at the component, asset, and system levels. The framework employs two key graphs: a network communication graph to model potential attack paths and identify the shortest routes to critical assets, and a system dependency graph to capture risk propagation from exploited vulnerabilities across interconnected components. Asset criticality and component dependency rules systematically assess and mitigate risks. Benchmarking against state-of-the-art methods demonstrates superior accuracy in vulnerability patch ranking, with enhanced explainability. This framework advances vulnerability management and sets the stage for future research in adaptive cybersecurity strategies.
Related papers
- Unveiling Zero-Space Detection: A Novel Framework for Autonomous Ransomware Identification in High-Velocity Environments [0.0]
The proposed Zero-Space Detection framework identifies latent behavioral patterns through unsupervised clustering and advanced deep learning techniques.
It operates effectively in high-velocity environments by integrating multi-phase filtering and ensemble learning for refined decision-making.
Experimental evaluation reveals high detection rates across diverse ransomware families, including LockBit, Conti, REvil, and BlackMatter.
arXiv Detail & Related papers (2025-01-22T11:41:44Z) - CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity [49.657358248788945]
Textual descriptions in cyber threat intelligence (CTI) reports are rich sources of knowledge about cyber threats.
Current CTI extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction.
We propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models.
arXiv Detail & Related papers (2024-10-28T14:18:32Z) - Cyber Knowledge Completion Using Large Language Models [1.4883782513177093]
Integrating the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) has expanded their cyber-attack surface.
Assessing the risks of CPSs is increasingly difficult due to incomplete and outdated cybersecurity knowledge.
Recent advancements in Large Language Models (LLMs) present a unique opportunity to enhance cyber-attack knowledge completion.
arXiv Detail & Related papers (2024-09-24T15:20:39Z) - FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks.
We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness.
Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
arXiv Detail & Related papers (2024-03-26T08:51:23Z) - Dynamic Vulnerability Criticality Calculator for Industrial Control Systems [0.0]
This paper introduces an innovative approach by proposing a dynamic vulnerability criticality calculator.
Our methodology encompasses the analysis of environmental topology and the effectiveness of deployed security mechanisms.
Our approach integrates these factors into a comprehensive Fuzzy Cognitive Map model, incorporating attack paths to holistically assess the overall vulnerability score.
arXiv Detail & Related papers (2024-03-20T09:48:47Z) - Profile of Vulnerability Remediations in Dependencies Using Graph
Analysis [40.35284812745255]
This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model.
We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities.
Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
arXiv Detail & Related papers (2024-03-08T02:01:47Z) - ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management [65.0114141380651]
ThreatKG is an automated system for OSCTI gathering and management.
It efficiently collects a large number of OSCTI reports from multiple sources.
It uses specialized AI-based techniques to extract high-quality knowledge about various threat entities.
arXiv Detail & Related papers (2022-12-20T16:13:59Z) - Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability
Management Framework [4.685954926214926]
Cyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems.
The current approaches are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecting vulnerabilities for mitigation.
We propose a novel framework, Deep VULMAN, consisting of a deep reinforcement learning agent and an integer programming method to fill this gap in the cyber vulnerability management process.
arXiv Detail & Related papers (2022-08-03T22:32:48Z) - A System for Automated Open-Source Threat Intelligence Gathering and
Management [53.65687495231605]
SecurityKG is a system for automated OSCTI gathering and management.
It uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors.
arXiv Detail & Related papers (2021-01-19T18:31:35Z) - Information Obfuscation of Graph Neural Networks [96.8421624921384]
We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data.
We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
arXiv Detail & Related papers (2020-09-28T17:55:04Z) - Graph Backdoor [53.70971502299977]
We present GTA, the first backdoor attack on graph neural networks (GNNs)
GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features.
It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
arXiv Detail & Related papers (2020-06-21T19:45:30Z)
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.