Αποτελέσματα Αναζήτησης
30 Αυγ 2023 · The paper aims to answer several questions related to graph-based approaches in network security, including the types of graphs used to represent network security data, the approaches used to analyze such graphs, the metrics used for detection and monitoring, and the reproducibility of existing works.
- A Review of Graph Approaches to Network Security Analytics
The following two sub-sections examine how various technical...
- A Review of Graph Approaches to Network Security Analytics
1 Ιουν 2024 · The integration of graph neural networks into intrusion detection systems has marked a significant stride in the realm of network security. Their inherent ability to process and analyze graph data offers a unique advantage, especially when dealing with complex network structures and patterns.
30 Νοε 2018 · The following two sub-sections examine how various technical approaches for graph-based network security align with these security phases and operational layers. 2.1 Phases of Security Operations. A phased approach to security helps provide defense in depth.
17 Αυγ 2024 · The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby...
3 Αυγ 2024 · We discuss the advantages of GNNs, including their capacity to integrate diverse data types, handle large-scale knowledge graphs, and reveal critical insights that aid in predicting and mitigating network security threats.
Firstly, the flow-level features of network traffic are populated and more information close to the features are selected to describe the network traffic; And then, the link homogeneity is used to establish the correlations between network traffic; Moreover, multi-head self-attention mechanism is introduced in the GCN model to provide larger wei...
7 Ιουλ 2023 · Network security data is intrinsically relational, and graph-structured data representations and Graph Neural Networks (GNNs) have the potential to drastically advance the AI4SEC domain. In this positioning paper we propose GRAPHSEC, a research agenda to systematically integrate GNNs in AI4SEC.
