File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.neucom.2020.10.055
- Scopus: eid_2-s2.0-85096165463
- WOS: WOS:000599909500009
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: State estimation of CPSs with deception attacks: Stability analysis and approximate computation
Title | State estimation of CPSs with deception attacks: Stability analysis and approximate computation |
---|---|
Authors | |
Keywords | Cyber-physical systems Deception attacks Stability analysis State estimation Unobservable attacks |
Issue Date | 2021 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/neucom |
Citation | Neurocomputing, 2021, v. 423, p. 318-326 How to Cite? |
Abstract | This paper is concerned with the state estimation problems of cyber-physical systems (CPSs) under unobservable deception attacks. First, the optimal state estimator is provided based on the derived state probability density function, which consists of an exponentially increasing number of linear Gaussian hypotheses. The exponentially growing number of components will lead to high computational cost. Therefore, a suboptimal state estimator based on the IMM algorithm is proposed, which is computationally more efficient than the optimal estimator. Finally, numerical results are given to verify the effectiveness and superiority of the proposed suboptimal estimator, rendering an efficient and stable state estimation when the privacy of sensor measurements is attacked. |
Persistent Identifier | http://hdl.handle.net/10722/304251 |
ISSN | 2023 Impact Factor: 5.5 2023 SCImago Journal Rankings: 1.815 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | ZHAO, C | - |
dc.contributor.author | Lam, J | - |
dc.contributor.author | Lin, H | - |
dc.date.accessioned | 2021-09-23T08:57:22Z | - |
dc.date.available | 2021-09-23T08:57:22Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Neurocomputing, 2021, v. 423, p. 318-326 | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304251 | - |
dc.description.abstract | This paper is concerned with the state estimation problems of cyber-physical systems (CPSs) under unobservable deception attacks. First, the optimal state estimator is provided based on the derived state probability density function, which consists of an exponentially increasing number of linear Gaussian hypotheses. The exponentially growing number of components will lead to high computational cost. Therefore, a suboptimal state estimator based on the IMM algorithm is proposed, which is computationally more efficient than the optimal estimator. Finally, numerical results are given to verify the effectiveness and superiority of the proposed suboptimal estimator, rendering an efficient and stable state estimation when the privacy of sensor measurements is attacked. | - |
dc.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/neucom | - |
dc.relation.ispartof | Neurocomputing | - |
dc.subject | Cyber-physical systems | - |
dc.subject | Deception attacks | - |
dc.subject | Stability analysis | - |
dc.subject | State estimation | - |
dc.subject | Unobservable attacks | - |
dc.title | State estimation of CPSs with deception attacks: Stability analysis and approximate computation | - |
dc.type | Article | - |
dc.identifier.email | Lam, J: jlam@hku.hk | - |
dc.identifier.authority | Lam, J=rp00133 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.neucom.2020.10.055 | - |
dc.identifier.scopus | eid_2-s2.0-85096165463 | - |
dc.identifier.hkuros | 325362 | - |
dc.identifier.volume | 423 | - |
dc.identifier.spage | 318 | - |
dc.identifier.epage | 326 | - |
dc.identifier.isi | WOS:000599909500009 | - |
dc.publisher.place | Netherlands | - |