Graph models for Cybersecurity -- A Survey
- URL: http://arxiv.org/abs/2311.10050v1
- Date: Thu, 16 Nov 2023 17:45:49 GMT
- Title: Graph models for Cybersecurity -- A Survey
- Authors: Jasmin Wachter,
- Abstract summary: We evaluate the current state of research for representing and analysing cyber-attack using graph models.
We propose a taxonomy on attack graph formalisms, based on 70 models.
Our taxonomy is especially designed to help users and applied researchers identify a suitable AG model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph models are helpful means of analyzing computer networks as well as complex system architectures for security. In this paper we evaluate the current state of research for representing and analysing cyber-attack using graph models, i.e. attack graph (AG) formalisms. We propose a taxonomy on attack graph formalisms, based on 70 models, which we analysed with respect to their \textit{graph semantic}, involved agents and analysis features. Additionally, we adress which formalisms allow for automatic attack graph generation from raw or processes data inputs. Our taxonomy is especially designed to help users and applied researchers identify a suitable AG model for their needs. A summary of the individual AG formalisms is provided as supplementary material.
Related papers
- Attacks on Node Attributes in Graph Neural Networks [32.40598187698689]
This research investigates the vulnerability of graph models through the application of feature based adversarial attacks.
Our findings indicate that decision time attacks using Projected Gradient Descent (PGD) are more potent compared to poisoning attacks that employ Mean Node Embeddings and Graph Contrastive Learning strategies.
arXiv Detail & Related papers (2024-02-19T17:52:29Z) - It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
Attack Graph (AG) represents the best-suited solution to model and analyze multi-step attacks on computer networks.
This paper introduces an analysis-driven framework for AG generation.
It enables real-time attack path analysis before the completion of the AG generation with a quantifiable statistical significance.
arXiv Detail & Related papers (2023-12-27T10:44:58Z) - A set of semantic data flow diagrams and its security analysis based on
ontologies and knowledge graphs [0.0]
This work considers two challenges: creating a set of machine-readable data flow diagrams that represent real cloud based applications; and usage domain specific knowledge for automatic analysis of the security aspects of such applications.
The set of 180 semantic diagrams (ontologies and knowledge graphs) is created based on cloud configurations (Docker Compose)
arXiv Detail & Related papers (2023-03-20T15:26:07Z) - Text Representation Enrichment Utilizing Graph based Approaches: Stock
Market Technical Analysis Case Study [0.0]
We propose a transductive hybrid approach composed of an unsupervised node representation learning model followed by a node classification/edge prediction model.
The proposed model is developed to classify stock market technical analysis reports, which to our knowledge is the first work in this domain.
arXiv Detail & Related papers (2022-11-29T11:26:08Z) - Model Inversion Attacks against Graph Neural Networks [65.35955643325038]
We study model inversion attacks against Graph Neural Networks (GNNs)
In this paper, we present GraphMI to infer the private training graph data.
Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.
arXiv Detail & Related papers (2022-09-16T09:13:43Z) - Question-Answer Sentence Graph for Joint Modeling Answer Selection [122.29142965960138]
We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs.
Online inference is then performed to solve the AS2 task on unseen queries.
arXiv Detail & Related papers (2022-02-16T05:59:53Z) - GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN.
Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features.
We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
arXiv Detail & Related papers (2021-06-05T07:07:52Z) - A Survey of Adversarial Learning on Graphs [59.21341359399431]
We investigate and summarize the existing works on graph adversarial learning tasks.
Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks.
We emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively.
arXiv Detail & Related papers (2020-03-10T12:48:00Z) - Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
Graph-based clustering plays an important role in the clustering area.
Recent studies about graph convolution neural networks have achieved impressive success on graph type data.
We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
arXiv Detail & Related papers (2020-02-20T10:11:28Z)
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.