arrow
返回

Automatic network restructuring and risk mitigation through business process asset dependency analysis

delete2020-09-01
delete12
PRE
AI
G
George Stergiopoulos
P
Panagiotis Dedousis
D
Dimitris Gritzalis *
DOI:10.1016/j.cose.2020.101869delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
In the 4th industrial revolution era, security of multiple interconnected devices has become a critical issue. A rapidly increasing number of cybersecurity incidents emerge due to complex interconnected sensors, devices, and systems used in the Internet of Things. In this paper, we tackle the need for automation in security risk analysis and restructuring of such networks. The presented framework models the connections of assets and devices so as to depict their interdependencies on a company's business processes and effectively reduces their overall risk against cybersecurity threats. It achieves this by (1) identifying critical components and dependency structural risks, (2) prioritizing assets based on their influence on business processes and (3) proposing network restructures and asset clusters. To do that, the proposed algorithm utilizes (i) dependency risk graphs for modeling and analyzing networks dependencies, (ii) graph minimum spanning trees, and (iii) network centrality metrics. We test the implementation on a real-world company and demonstrate its effectiveness. Results show that the framework can automatically identify critical components and dependency structural risks and propose different network topologies by creating the optimum number of asset subnets, while retaining business operations. Tests show that the closeness centrality metric combined with the midpoint on extreme values calculation type works best for network asset grouping and subnetting. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Risk assessment
Risk mitigation
Business impact
Dependency risk graphs
Graph centrality
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

C
Computers and Security
IF:
5.4
论文数:
4.6K
被引数:
1.4W

机构

暂无机构信息