A review on graph-based approaches for network security monitoring and botnet detection
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | International Journal of Information Security |
| MU Faculty or unit | |
| Citation | |
| web | https://link.springer.com/article/10.1007/s10207-023-00742-7 |
| Doi | http://dx.doi.org/10.1007/s10207-023-00742-7 |
| Keywords | Graph theory;Machine learning;Network security;Botnet detection;Monitoring;Cybersecurity |
| Description | This survey paper provides a comprehensive overview of recent research and development in network security that uses graphs and graph-based data representation and analytics. The paper focuses on the graph-based representation of network traffic records and the application of graph-based analytics in intrusion detection and botnet detection. 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. The paper presents a survey of graph models used to represent, store, and visualize network security data, a survey of the algorithms and approaches used to analyze such data, and an enumeration of the most important graph features used for network security analytics for monitoring and botnet detection. The paper also discusses the challenges and limitations of using graph-based approaches in network security and identifies potential future research directions. Overall, this survey paper provides a valuable resource for researchers and practitioners in the field of network security who are interested in using graph-based approaches for analyzing and detecting malicious activities in networks. |
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