UM
Residential Collegefalse
Status已發表Published
Defending against advanced persistent threat: A risk management perspective
Zhong X.3; Yang L.-X.1; Yang X.3; Xiong Q.3; Wen J.3; Tang Y.Y.4
2018
Conference Name1st International Conference on Science of Cyber Security (SciSec)
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11287 LNCS
Pages207-215
Conference DateAUG 12-14, 2018
Conference PlaceChinese Acad Sci, Inst Informat Engn, Beijing, PEOPLES R CHINA
Abstract

Advanced persistent threat (APT) as a new form of cyber attack has posed a severe threat to modern organizations. When an APT has been detected, the target organization has to develop a response resource allocation strategy to mitigate her potential loss. This paper suggests a risk management approach to solving this APT response problem. First, we present three state evolution models. Thereby we assess the organization’s potential loss. On this basis, we propose two kinds of game-theoretic models of the APT response problem. This work initiates the study of the APT response problem.

KeywordAdvanced Persistent Threat Apt Response Problem Game Theory Risk Assessment Risk Management State Evolution Model
DOI10.1007/978-3-030-03026-1_16
URLView the original
Language英語English
WOS IDWOS:000917197600016
Scopus ID2-s2.0-85057811549
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Deakin University
2.Universidade de Macau
3.Chongqing University
4.Beihang University
Recommended Citation
GB/T 7714
Zhong X.,Yang L.-X.,Yang X.,et al. Defending against advanced persistent threat: A risk management perspective[C], 2018, 207-215.
APA Zhong X.., Yang L.-X.., Yang X.., Xiong Q.., Wen J.., & Tang Y.Y. (2018). Defending against advanced persistent threat: A risk management perspective. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11287 LNCS, 207-215.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhong X.]'s Articles
[Yang L.-X.]'s Articles
[Yang X.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhong X.]'s Articles
[Yang L.-X.]'s Articles
[Yang X.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhong X.]'s Articles
[Yang L.-X.]'s Articles
[Yang X.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.