EEVi – framework for evaluating the effectiveness of visualization in cyber-security
EEVi – framework for evaluating the effectiveness of visualization in cyber-security
Cyber-security visualization is an up-and-coming area which aims to reduce security analysts’ workload by presenting information as visual analytics rather than a string of text and characters. But the adoption of the resultant visualizations has not increased. The literature indicates a research gap of a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for it. Therefore, this research addresses the research gap by developing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ can be defined as the features of visualization that are crucial to perform a certain task successfully. EEVi has been developed by analyzing qualitative data that leads to the formation of cognitive relationships (called links) between data that act as guidelines for effective cyber-security visualization in terms of the performed task. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis presents a glimpse into the usage of EEVi in cyber-security visualization.
340-345
Sethi, Aneesha
d28f4d06-34fe-4b65-b816-d92ccbbff6f3
Paci, Federica
9fbf3e5b-ae03-40e8-a75a-3657cbc9216e
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
16 February 2017
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
(2017)
EEVi – framework for evaluating the effectiveness of visualization in cyber-security.
The 11th International Conference for Internet Technology and Secured Transactions (ICITST-2016), Barcelona, Spain.
05 - 07 Dec 2016.
.
(doi:10.1109/ICITST.2016.7856726).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Cyber-security visualization is an up-and-coming area which aims to reduce security analysts’ workload by presenting information as visual analytics rather than a string of text and characters. But the adoption of the resultant visualizations has not increased. The literature indicates a research gap of a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for it. Therefore, this research addresses the research gap by developing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ can be defined as the features of visualization that are crucial to perform a certain task successfully. EEVi has been developed by analyzing qualitative data that leads to the formation of cognitive relationships (called links) between data that act as guidelines for effective cyber-security visualization in terms of the performed task. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis presents a glimpse into the usage of EEVi in cyber-security visualization.
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EEVi - Eprints.pdf
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Accepted/In Press date: 4 October 2016
Published date: 16 February 2017
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© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Venue - Dates:
The 11th International Conference for Internet Technology and Secured Transactions (ICITST-2016), Barcelona, Spain, 2016-12-05 - 2016-12-07
Organisations:
Electronic & Software Systems
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Local EPrints ID: 402853
URI: http://eprints.soton.ac.uk/id/eprint/402853
PURE UUID: 296063b3-2856-446b-9add-016387261ad5
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Date deposited: 14 Nov 2016 16:44
Last modified: 16 Mar 2024 02:51
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Contributors
Author:
Aneesha Sethi
Author:
Federica Paci
Author:
Gary Wills
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