EEVi –Framework and Guidelines to Evaluate the Effectiveness of Cyber-Security Visualization
EEVi –Framework and Guidelines to Evaluate the Effectiveness of Cyber-Security Visualization
Cyber-security visualization aims to reduce security analysts’ workload by presenting information as visual analytics instead of a string of text and characters. However, the adoption of the resultant visualizations by security analysts, is not widespread. The literature indicates a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for the low adoption rate. Consequently, this article addresses the research gap by introducing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ is defined as the features of visualization that are critical for an analyst to competently perform a certain task. EEVi has been developed by analyzing qualitative data which led to the formation of cognitive relationships (called links) between data. These relationships acted as guidelines for effective cyber-security visualization to perform tasks. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis of the framework presented, demonstrates how EEVi can be put into practice using the guidelines for effective cyber- security visualization. The guidelines can be used to guide visualization developers to create effective visualizations for security analysts based on their requirements.
761-770
Sethi, Aneesha
d28f4d06-34fe-4b65-b816-d92ccbbff6f3
Paci, Federica
9fbf3e5b-ae03-40e8-a75a-3657cbc9216e
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
December 2016
Sethi, Aneesha
d28f4d06-34fe-4b65-b816-d92ccbbff6f3
Paci, Federica
9fbf3e5b-ae03-40e8-a75a-3657cbc9216e
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Sethi, Aneesha, Paci, Federica and Wills, Gary
(2016)
EEVi –Framework and Guidelines to Evaluate the Effectiveness of Cyber-Security Visualization.
International Journal of Intelligent Computing Research (IJICR), 7 (4), .
(doi:10.20533/ijicr.2042.4655.2016.0094).
Abstract
Cyber-security visualization aims to reduce security analysts’ workload by presenting information as visual analytics instead of a string of text and characters. However, the adoption of the resultant visualizations by security analysts, is not widespread. The literature indicates a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for the low adoption rate. Consequently, this article addresses the research gap by introducing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ is defined as the features of visualization that are critical for an analyst to competently perform a certain task. EEVi has been developed by analyzing qualitative data which led to the formation of cognitive relationships (called links) between data. These relationships acted as guidelines for effective cyber-security visualization to perform tasks. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis of the framework presented, demonstrates how EEVi can be put into practice using the guidelines for effective cyber- security visualization. The guidelines can be used to guide visualization developers to create effective visualizations for security analysts based on their requirements.
Text
EEVi-Eprints
- Accepted Manuscript
More information
e-pub ahead of print date: 1 December 2016
Published date: December 2016
Additional Information:
The original source for the publication is the 'International Journal of Intelligent Computing Research' and the publisher is 'Infonomics Society'.
Organisations:
Electronics & Computer Science, Electronic & Software Systems
Identifiers
Local EPrints ID: 406173
URI: http://eprints.soton.ac.uk/id/eprint/406173
ISSN: 2042-4655
PURE UUID: 87c0024f-562c-446b-aca9-37804d6df54e
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Date deposited: 10 Mar 2017 10:41
Last modified: 16 Mar 2024 05:06
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Contributors
Author:
Aneesha Sethi
Author:
Federica Paci
Author:
Gary Wills
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