| Autor: |
Goodall JR, Ragan ED, Steed CA, Reed JW, Richardson GD, Huffer KMT, Bridges RA, Laska JA |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on visualization and computer graphics [IEEE Trans Vis Comput Graph] 2018 Aug 20. Date of Electronic Publication: 2018 Aug 20. |
| DOI: |
10.1109/TVCG.2018.2865029 |
| Abstrakt: |
Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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