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However, this proliferation of data also exposes these systems to significant cyber-physical security threats. For instance, malicious attackers may delete, change, or replace original data, leading to defective products, damaged equipment, or operational safety hazards. False data injection attacks can compromise machine learning models, resulting in erroneous predictions and decisions. To mitigate these risks, it is crucial to employ robust data processing techniques that can adapt to varying process conditions and detect anomalies in real-time. In this context, the incremental machine learning (IML) approaches can be valuable, allowing models to be updated incrementally with newly collected data without retraining from scratch. Moreover, although recent studies have demonstrated the potential of blockchain in enhancing data security within manufacturing systems, most existing security frameworks are primarily based on cryptography, which does not sufficiently address data quality issues. Thus, this study proposes a gatekeeper mechanism to integrate IML with blockchain and discusses how this integration could potentially increase the data integrity of cyber-enabled manufacturing systems. The proposed IML-integrated blockchain can address the data security concerns from both intentional alterations (e.g., malicious tampering) and unintentional alterations (e.g., process anomalies and outliers). The real-world case study results show that the proposed gatekeeper integration algorithm can successfully filter out over 80% of malicious data entries while maintaining comparable classification performance to standard IML models. Furthermore, the integration of blockchain enables effective detection of tampering attempts, ensuring the trustworthiness of the stored information.<\/jats:p>","DOI":"10.1115\/1.4067736","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T09:39:21Z","timestamp":1737970761000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":4,"title":["Incremental Machine Learning-Integrated Blockchain for Real-Time Security Protection in Cyber-Enabled Manufacturing Systems"],"prefix":"10.1115","volume":"25","author":[{"given":"Boris","family":"Oskolkov","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01g9vbr38","id-type":"ROR","asserted-by":"publisher"}],"name":"Oklahoma State University The School of Industrial Engineering & Management, , , \u00a0","place":["Stillwater, OK, 74078"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Kan","sequence":"additional","affiliation":[{"name":"The University of Texas at Arlington Department of Industrial, Manufacturing, and Systems Engineering, , , \u00a0","place":["Arlington, TX, 76019"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenmeng","family":"Tian","sequence":"additional","affiliation":[{"name":"Mississippi State University Department of Industrial and Systems Engineering, , , MS\u00a0","place":["Mississippi State, 39762"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew Chung Chee","family":"Law","sequence":"additional","affiliation":[{"name":"IoTeX , , \u00a0","place":["Palo Alto, CA, 94306"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenang","family":"Liu","sequence":"additional","affiliation":[{"name":"Oklahoma State University The School of Industrial Engineering & Management, , , \u00a0","place":["Stillwater, OK, 74078"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"issue":"4","key":"2026030618420178600_CIT0001","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1007\/s11227-016-1677-z","article-title":"Handling Big Data: Research Challenges and Future Directions","volume":"72","author":"Anagnostopoulos","year":"2016","journal-title":"J. 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