Residential College | false |
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 Name | 1st International Conference on Science of Cyber Security (SciSec) |
Source Publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 11287 LNCS |
Pages | 207-215 |
Conference Date | AUG 12-14, 2018 |
Conference Place | Chinese 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. |
Keyword | Advanced Persistent Threat Apt Response Problem Game Theory Risk Assessment Risk Management State Evolution Model |
DOI | 10.1007/978-3-030-03026-1_16 |
URL | View the original |
Language | 英語English |
WOS ID | WOS:000917197600016 |
Scopus ID | 2-s2.0-85057811549 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | University of Macau |
Affiliation | 1.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. |
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