{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T23:57:15Z","timestamp":1769385435770,"version":"3.49.0"},"reference-count":17,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Helmholtz Association of German Research Centres (HGF) Program-Oriented Funding POF IV, program: Energy Systems Design"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>A novel hybrid method of data reconciliation and gross error detection, applicable for systems with a mixture of dynamic and static system constraints, is developed for the detection of cyber attacks. The requirements for the new application of data reconciliation and similar methods in cybersecurity differ from the requirements for the established use of data reconciliation in automation and control engineering. For the detection of cyber attacks aiming at physical damage the main focus is on significant gross error detection while for classical applications a robust optimization and smoothing of measurement data is the main concern. Therefore the new hybrid method of direct discrete dynamic data reconciliation, as well as similar methods of data reconciliation and Kalman filters with their referring methods of gross error detection are evaluated regarding their aptitude for attack detection in cybersecurity. All considered methods are compared regarding properties resulting from the specific optimization procedure and the detection. The new direct discrete dynamic data reconciliation is indeed shown to outperform the other methods regarding the detection of cyber attacks.<\/jats:p>","DOI":"10.1515\/auto-2025-0076","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T16:29:04Z","timestamp":1767976144000},"page":"35-46","source":"Crossref","is-referenced-by-count":0,"title":["D4R: a new direct discrete dynamic data reconciliation method for\u00a0the detection of\u00a0cyber attacks"],"prefix":"10.1515","volume":"74","author":[{"given":"Kathrin","family":"Reibelt","sequence":"first","affiliation":[{"name":"150232 KIT: Karlsruher Institut fur Technologie, IAI: Institute for Automation and Applied Informatics , Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen , Baden-W\u00fcrttemberg , Germany"}]},{"given":"J\u00f6rg","family":"Matthes","sequence":"additional","affiliation":[{"name":"150232 KIT: Karlsruher Institut fur Technologie, IAI: Institute for Automation and Applied Informatics , Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen , Baden-W\u00fcrttemberg , Germany"}]},{"given":"Veit","family":"Hagenmeyer","sequence":"additional","affiliation":[{"name":"150232 KIT: Karlsruher Institut fur Technologie, IAI: Institute for Automation and Applied Informatics , Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen , Baden-W\u00fcrttemberg , Germany"}]}],"member":"374","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"2026012405585787837_j_auto-2025-0076_ref_001","unstructured":"K. Reibelt, \u201cEin neues Verfahren zur Detektion von Cyber-Angriffen auf zuk\u00fcnftige Energiesysteme unter Nutzung physikalischer Modellans\u00e4tze,\u201d Doctoral thesis, Karlsruhe Institute of Technology, 2025. Available at: https:\/\/www.publikationen.bibliothek.kit.edu\/1000183407."},{"key":"2026012405585787837_j_auto-2025-0076_ref_002","doi-asserted-by":"crossref","unstructured":"S. Bai, J. Thibault, and D. D. McLean, \u201cDynamic data reconciliation: Alternative to Kalman filter,\u201d J. Process Control, vol.\u00a016, no.\u00a05, pp.\u00a0485\u2013498, 2006, https:\/\/doi.org\/10.1016\/j.jprocont.2005.08.002.","DOI":"10.1016\/j.jprocont.2005.08.002"},{"key":"2026012405585787837_j_auto-2025-0076_ref_003","doi-asserted-by":"crossref","unstructured":"S. Narasimhan and C. Jordache, Data Reconciliation and Gross Error Detection, Houston, Texas, Gulf Professional Publishing, 1999.","DOI":"10.1016\/B978-088415255-2\/50002-1"},{"key":"2026012405585787837_j_auto-2025-0076_ref_004","doi-asserted-by":"crossref","unstructured":"M. J. Leibman, T. F. Edgar, and L. S. Lasdon, \u201cEfficient data reconciliation and estimation for dynamic processes using nonlinear programming techniques,\u201d Comput. Chem. Eng., vol.\u00a016, nos. 10\u201311, pp.\u00a0963\u2013986, 1992, https:\/\/doi.org\/10.1016\/0098-1354(92)80030-D.","DOI":"10.1016\/0098-1354(92)80030-D"},{"key":"2026012405585787837_j_auto-2025-0076_ref_005","doi-asserted-by":"crossref","unstructured":"A. C. Tamhane, \u201cA note on the use of residuals for detecting an outlier in linear regression,\u201d Biometrika, vol.\u00a069, no.\u00a02, pp.\u00a0488\u2013489, 2010, https:\/\/doi.org\/10.2307\/2335429.","DOI":"10.1093\/biomet\/69.2.488"},{"key":"2026012405585787837_j_auto-2025-0076_ref_006","unstructured":"G. Almasy and T. Sztano, \u201cChecking and correction of measurements on the basis of linear system model,\u201d Probl. Control Inf. Theory, vol. 4, pp. 57\u201369, 1975."},{"key":"2026012405585787837_j_auto-2025-0076_ref_007","doi-asserted-by":"crossref","unstructured":"J. Rosenberg, R. S. H. Mah, and C. Iordache, \u201cEvaluation of schemes for detecting and identifying gross errors in process data,\u201d Ind. Eng. Chem. Res., vol.\u00a026, no.\u00a03, pp.\u00a0555\u2013564, 1987, https:\/\/doi.org\/10.1021\/ie00063a023.","DOI":"10.1021\/ie00063a023"},{"key":"2026012405585787837_j_auto-2025-0076_ref_008","doi-asserted-by":"crossref","unstructured":"S. Narasimhan and R. S. H. Mah, \u201cGeneralized likelihood ratio method for gross error identification,\u201d AIChE J., vol. 33, no. 9, pp. 1514\u20131521, 1987. https:\/\/doi.org\/10.1002\/aic.690330911.","DOI":"10.1002\/aic.690330911"},{"key":"2026012405585787837_j_auto-2025-0076_ref_009","doi-asserted-by":"crossref","unstructured":"R. S. H. Mah, G. M. Stanley, and D. M. Downing, \u201cReconciliation and rectification of process flow and inventory data,\u201d Ind. Eng. Chem. Process Des. Dev., vol. 15, no. 1, pp. 175\u2013183, 1976. https:\/\/doi.org\/10.1021\/i260057a030.","DOI":"10.1021\/i260057a030"},{"key":"2026012405585787837_j_auto-2025-0076_ref_010","unstructured":"K. Reibelt, H. B. Keller, V. Hagenmeyer, and J. Matthes, \u201cDynamic model based detection of cyberattacks in industrial facilities,\u201d in Computer Aided Chemical Engineering 50, 31st European Symposium on Computer Aided Process Engineering: ESCAPE-31, M. T\u00fcrkay, Ed., 2021."},{"key":"2026012405585787837_j_auto-2025-0076_ref_011","doi-asserted-by":"crossref","unstructured":"D. K. Rolins and S. Devanathan, \u201cUnbiased estimation in dynamic data reconciliation,\u201d AIChE J., vol. 39, no. 8, pp. 1330\u20131334, 1993. https:\/\/doi.org\/10.1002\/aic.690390809.","DOI":"10.1002\/aic.690390809"},{"key":"2026012405585787837_j_auto-2025-0076_ref_012","doi-asserted-by":"crossref","unstructured":"M. Darouach and M. Zasadzinski, \u201cData reconciliation in generalized linear dynamic systems,\u201d AlQChE J., vol. 37, no. 2, pp. 193\u2013201, 1991. https:\/\/doi.org\/10.1002\/aic.690370205.","DOI":"10.1002\/aic.690370205"},{"key":"2026012405585787837_j_auto-2025-0076_ref_013","doi-asserted-by":"crossref","unstructured":"W. Yin, R. Gang, and W. Shuqing, \u201cLinear dynamic data reconciliation: Refinery application,\u201d IFAC Proc., vol.\u00a034, no.\u00a025, pp.\u00a0561\u2013566, 2001, https:\/\/doi.org\/10.1016\/S1474-6670(17)33883-1.","DOI":"10.1016\/S1474-6670(17)33883-1"},{"key":"2026012405585787837_j_auto-2025-0076_ref_014","doi-asserted-by":"crossref","unstructured":"M. Bagajewicz and Q. Jiang, \u201cAn integral approach to plant linear dynamic reconciliation,\u201d AlChE J., vol. 43, no. 10, pp. 2546\u20132558, 1997. https:\/\/doi.org\/10.1002\/aic.690431016.","DOI":"10.1002\/aic.690431016"},{"key":"2026012405585787837_j_auto-2025-0076_ref_015","doi-asserted-by":"crossref","unstructured":"O. Bennouna, N. Heraud, M. Rodriguez, and H. Camblong, \u201cData reconciliation & gross error detection applied to wind power,\u201d Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng., vol. 221, no. 3, pp. 497\u2013506, 2007. https:\/\/doi.org\/10.1243\/09596518JSCE266.","DOI":"10.1243\/09596518JSCE266"},{"key":"2026012405585787837_j_auto-2025-0076_ref_016","unstructured":"R. Marchthaler and S. Dingler, Kalman-Filter \u2013 Einf\u00fchrung in die Zustandssch\u00e4tzung und ihre Anwendung f\u00fcr eingebettete Systeme, Wiesbaden, Germany, Springer Vieweg, 2017."},{"key":"2026012405585787837_j_auto-2025-0076_ref_017","doi-asserted-by":"crossref","unstructured":"S. Bai, J. Thibault, and D. D. McLean, \u201cSimultaneous measurement bias correction and dynamic data reconciliation,\u201d Can. J. Chem. Eng., vol.\u00a085, no.\u00a01, pp.\u00a0111\u2013117, 2007, https:\/\/doi.org\/10.1002\/cjce.5450850111.","DOI":"10.1002\/cjce.5450850111"}],"container-title":["at - Automatisierungstechnik"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/auto-2025-0076\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/auto-2025-0076\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T05:59:28Z","timestamp":1769234368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/auto-2025-0076\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,9]]},"published-print":{"date-parts":[[2026,1,23]]}},"alternative-id":["10.1515\/auto-2025-0076"],"URL":"https:\/\/doi.org\/10.1515\/auto-2025-0076","relation":{},"ISSN":["0178-2312","2196-677X"],"issn-type":[{"value":"0178-2312","type":"print"},{"value":"2196-677X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]}}}