dc.contributor.author | Dahan, Mathieu | |
dc.contributor.author | Sela, Lina | |
dc.contributor.author | Amin, Saurabh | |
dc.date.accessioned | 2023-03-07T16:56:15Z | |
dc.date.available | 2023-03-07T16:56:15Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/148394 | |
dc.description.abstract | <jats:p> Ensuring the security of critical infrastructures is crucial for the society’s welfare and prosperity. However, these infrastructure networks are inherently vulnerable to both intentional and unintentional threats. In “Network Inspection for Detecting Strategic Attacks,” Dahan, Sela, and Amin study a strategic network inspection problem, formulated as a large-scale bilevel optimization problem, in which a utility seeks to determine an inspection strategy with minimum number of smart detectors that ensures a desirable expected detection performance under worst-case attacks. The authors derive structural properties of optimal solutions and show that the problem can be solved using Nash equilibria of a large-scale zero-sum game. Their analysis leads to a computationally tractable and operationally feasible solution approach with theoretical guarantees based on combinatorial objects that capture the nature of equilibrium inspection and attack strategies. Their computational study indicates that utilities can achieve a high level of protection in large-scale networks by strategically positioning a small number of detectors. </jats:p> | en_US |
dc.language.iso | en | |
dc.publisher | Institute for Operations Research and the Management Sciences (INFORMS) | en_US |
dc.relation.isversionof | 10.1287/OPRE.2021.2180 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | Network Inspection for Detecting Strategic Attacks | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Dahan, Mathieu, Sela, Lina and Amin, Saurabh. 2022. "Network Inspection for Detecting Strategic Attacks." Operations Research, 70 (2). | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering | en_US |
dc.relation.journal | Operations Research | en_US |
dc.eprint.version | Original manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2023-03-07T16:48:35Z | |
dspace.orderedauthors | Dahan, M; Sela, L; Amin, S | en_US |
dspace.date.submission | 2023-03-07T16:48:36Z | |
mit.journal.volume | 70 | en_US |
mit.journal.issue | 2 | en_US |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |