arrow
返回

Complex Attack Linkage Decision-Making in Edge Computing Network

delete2019-01-01
delete20
delete
OA
AI
Q
Qianmu Li
孟顺梅 (Shunmei Meng)
S
Sainan Zhang
J
Jun Hou
L
Lianyong Qi *
DOI:10.1109/ACCESS.2019.2891505delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
The edge computing network refers to a new paradigm of edge-side big data computing networks, which integrates networks, computing, storage, and business core capabilities. It is close to users, the Internet of Things (IoT), or data source side. The edge computing network is generated by the common development of cloud computing and the IoT. The core is the massive uplink monitoring collection and downlink decision-making control big data generated by intelligent sensing devices, solving the problem of low data computing efficiency and performance under the centralized cloud computing model. Compared with traditional cloud computing networks, the edge computing network has more abundant terminal types, more frequent data real-time interaction, more complex transmission network technology systems, and more intelligent and interconnected business systems. Moreover, this situation is aggravated with the mobile edge computing, e.g., model proximity service increasingly prevalent in daily life. However, the ubiquitous and open features of edge computing networks expose network security risks to all parts of the system, facing severe security protection challenges. To solve the linkage disposal and minimum cost response of complex attacks, we propose an attack linkage disposal decision-making method for edge computing network systems based on attribute attack graphs. A simplified attribute attack graph is constructed through the network security alarm association and false-alarm determination, and formal correlation analysis is performed on the causal relationship of the alarm information. On this basis, the linkage defense strategy decision computing is transformed into the minimum dominance set solution of the attribute attack graph. Finally, a linkage disposal strategy execution point decision algorithm based on the greedy algorithm is designed, which constructs a set of attack linkage disposal decision-making technologies with optimal defense cost. It provides a powerful guarantee for timely and effectively active defense.
Keyword:
Edge computing network
complex attack detection
attribute attack graph
linkage defense
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

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

W
Wuyi University
学者数:
4.3K
论文数: 2.3K
被引数: 2.9K
Q
Qufu Normal University
学者数:
7.8K
论文数: 5.8K
被引数: 5.4K
引用论文

引用论文

Reconfigurable Security: Edge-Computing-Based Framework for IoT
err2018-09-01
err56
errOAAI
errHsu, Ruei-Hau; Lee, Jemin; Quek, Tony Q. S.; Chen, Jyh-Cheng
err分享
err收藏
err分享
err收藏
err分享
err收藏
A Vulnerability Assessment Method in Industrial Internet of Things Based on Attack Graph and Maximum Flow
err2018-01-01
err91
errOAAI
errWang, Huan; Chen, Zhanfang; Zhao, Jianping; Di, Xiaoqiang; Liu, Dan
err分享
err收藏
A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2.
err
IF0
err2020-08-04
err0
errOAAI
errSean Nolan; Marissa Vignali; Mark Klinger; Jennifer N. Dines; Ian M. Kaplan; Emily Svejnoha; Tracy Craft; Katie Boland; Mitch Pesesky; Rachel M. Gittelman; Thomas M. Snyder; Christopher J. Gooley; Simona Semprini; Claudio Cerchione; Massimiliano Mazza; Ottavia M. Delmonte; Kerry Dobbs; Gonzalo Carreño-Tarragona; Santiago Barrio; Vittorio Sambri; Giovanni Martinelli; Jason D. Goldman; James R. Heath; Luigi D. Notarangelo; Jonathan M. Carlson; Joaquin Martinez-Lopez; Harlan S. Robins
err分享
err收藏
A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds
err2018-09-01
err62
errOAAI
errHu, Chunqiang; Li, Wei; Cheng, Xiuzhen; Yu, Jiguo; Wang, Shengling; Bie, Rongfang
err分享
err收藏
学者 查看更多内容