Abstract
We introduce the notion of Quality of Indicator (QoI) to assess the level of contribution by participants in threat intelligence sharing. We exemplify QoI by metrics of the correctness, relevance, utility, and uniqueness of indicators. We build a system that extrapolates the metrics using a machine learning process over a reference set of indicators. We compared these results against a model that only considers the volume of information as a metric for contribution, and unveiled various observations, including the ability to spot low-quality contributions that are synonymous to free-riding.
Original language | English |
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Title of host publication | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6951-6955 |
Number of pages | 5 |
ISBN (Print) | 9781538646588 |
DOIs | |
Publication status | Published - 10 Sept 2018 |
Event | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2018-April |
ISSN (Print) | 1520-6149 |
Conference
Conference | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 |
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Country/Territory | Canada |
City | Calgary |
Period | 15/04/18 → 20/04/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- QoI
- Sharing
- Threat intelligence