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This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security.<\/jats:p>","DOI":"10.1007\/s44227-024-00029-w","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T20:36:37Z","timestamp":1716928597000},"page":"237-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Enhancing IoT Security: A Blockchain-Based Mitigation Framework for Deauthentication Attacks"],"prefix":"10.1007","volume":"12","author":[{"given":"S. Harihara","family":"Gopalan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Manikandan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. 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