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The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. 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. [ABSTRACT FROM AUTHOR]

Breast cancer has emerged as one of the most prevalent malignancies affecting women today, underscoring the critical need for advanced diagnostic tools. Mammography has been the conventional method for early breast cancer detection, yet recent years have witnessed the emergence of thermal infrared scans, or thermographies, as a potential contender for diagnosing breast cancer, particularly in cases of dense breast tissue. Thermographic images reveal higher temperatures in regions containing tumors compared to healthy breast tissue, providing a promising avenue for early diagnosis. In parallel, the field of radiography has seen the advent of deep learning (DL) techniques, offering a computational approach to breast cancer identification. This study presents a novel approach, the Enhanced Deep learning-based Convolutional Neural Network (EDCNN), aimed at generating heatmaps from two-dimensional thermal breast images. These heatmaps are employed to quantitatively assess breast vascularity, yielding interpretable parameters for further analysis. In addition, the study proposes a classifier that predicts the likelihood of breast cancer purely based on these extracted parameters. To enhance the accuracy of this process, the algorithm combines Fuzzy C-means clustering with the Region of Interest (ROI) technique to effectively isolate the breast from surrounding body parts. The segmentation results are evaluated using temperature profiles, revealing substantial peaks in the patterns as indicators of ROIs. This identification of hot areas hints at the potential presence of a tumor. To validate the effectiveness of this approach, the study constructs DL models using convolutional neural networks, training them with thermal breast images from the Graphical DMR datasets. The results are compelling, with the EDCNN models outperforming alternative methods, achieving an impressive accuracy of 96.8% and a specificity rate of 93.7%. This research thus offers a robust, efficient, and reliable means of early breast cancer diagnosis, marking a significant advancement in the field. [ABSTRACT FROM AUTHOR]