ＩｏＴ機器向けの軽量化暗号実装技術とユーザ生体継続認証への応用

在本财年，我们将向量机算法与模糊粗糙集理论相结合，用于安全的用户生物特征认证，比传统的FaceID和TouchID更安全。传统的核心向量机和支持向量机在用于数据分类时存在一定的局限性，而模糊粗糙集理论的加入可以动态调整隶属函数的程度，优化每个特征的权重分配，进一步提高分类精度。提出了一种结合支持向量机和模糊粗糙集的新算法，利用支持向量机和模糊粗糙集相结合的领域生成算法来训练和识别由支持向量机和模糊粗糙集相结合的领域生成的恶意域，利用在线和增量算法自动识别和分类不存在的领域。实验表明，该算法确实可以达到99%以上的高分类准确率。本文还提出了一种结合云支持向量机和模糊粗糙集的新算法，用于通过生物特征和行为特征来训练和识别登录和认证的用户。我们的应用使得医疗云为同一家医院内或不同医院之间的医疗数据共享带来了更多的便利，对未经授权的访问也更加安全。我们从医生自己的手势中获取生物和行为特征进行训练和分类，以确保只有授权的医生才能访问患者数据。

In this fiscal year, we combined vector machine algorithms with fuzzy rough sets theory in our secure user biometrics authentication, which is more secure than traditional FaceID and TouchID. Traditional core vector machine (CVM) and support vector machine (SVM) have some limitations when used for data classification, while the addition of fuzzy rough sets theory can dynamically adjust the degree of the membership function, optimizing the weight distribution of each feature, and further improving the classification accuracy.We developed a new algorithm combined with SVM and fuzzy rough sets are used to train and identify malicious domain generated by domain generation algorithms combined SVM with fuzzy rough sets, using online and incremental algorithms to automatically identify and classify non-existent domains as benign or malicious. Experiments show that the algorithm can indeed achieve a high classification accuracy, reaching more than 99%.We also develop a new algorithm combined with CVM and fuzzy rough sets used to train and identify users who login and authenticate through biometric and behavioral characteristics. Our application makes the medical cloud bring more convenience to share medical data within the same hospital or between different hospitals and more secure to unauthorized access. We obtain biological and behavioral characteristics from doctors' own gestures for training and classifying, to ensure that only authorized doctors can access patient data.