Visible to the public Biblio

Filters: Author is Shi, Ronghua  [Clear All Filters]
2017-03-20
Shi, Yang, Zhang, Yaoxue, Zhou, Fangfang, Zhao, Ying, Wang, Guojun, Shi, Ronghua, Liang, Xing.  2016.  IDSPlanet: A Novel Radial Visualization of Intrusion Detection Alerts. Proceedings of the 9th International Symposium on Visual Information Communication and Interaction. :25–29.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.

Shi, Yang, Zhang, Yaoxue, Zhou, Fangfang, Zhao, Ying, Wang, Guojun, Shi, Ronghua, Liang, Xing.  2016.  IDSPlanet: A Novel Radial Visualization of Intrusion Detection Alerts. Proceedings of the 9th International Symposium on Visual Information Communication and Interaction. :25–29.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.