Association Analysis Of Cyber-Attack Attribution Based On Threat Intelligence
- DOI
- 10.2991/jimec-17.2017.49How to use a DOI?
- Keywords
- Association Analysis; Threat Intelligence; Cyber-attack Attribution; Constraint Analysis
- Abstract
This paper presented an association analysis method in cyber-attack attribution based on threat intelligence. The method used the local advantage model to analyse the data related to threat intelligence in cyber-attack attribution by combining the intrusion kill chains model and F2T2EA model. Then, this paper introduced and explained association analysis as well as association analysis flow. This flow was composed of four parts: input, association analysis, constraint analysis and output. Then, four types of association analysis were introduced: based on statistic, based on extension, based on behavior pattern and based on probability similarity. Considering about that association analysis is a cyclic iteration process, hierarchical constraint, object constraint, feedback constraint and merged constraint are recommended in detail. Finally, the proposed association analysis method was used in a real emergency response case of targeted attack. The result of case study showed that we can find out much useful information for cyber-attack attribution association analysis based on threat intelligence.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qiang Li AU - Zeming Yang AU - Zhengwei Jiang AU - Baoxu Liu AU - Yuxia Fu PY - 2017/10 DA - 2017/10 TI - Association Analysis Of Cyber-Attack Attribution Based On Threat Intelligence BT - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017) PB - Atlantis Press SP - 222 EP - 230 SN - 2352-538X UR - https://doi.org/10.2991/jimec-17.2017.49 DO - 10.2991/jimec-17.2017.49 ID - Li2017/10 ER -