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结合在安全用户生物识别认证中，比传统的FaceID和TouchID更安全。传统的核心向量机（CVM）和支持向量机（SVM）在进行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度函数的大小，优化各特征的权重分布，进一步提高分类精度。我们开发了一种新的SVM和模糊粗糙集相结合的算法，利用模糊粗糙集训练和识别由SVM和模糊粗糙集相结合的域生成算法生成的恶意域，使用在线和增量算法自动识别和分类不存在的域为良性或恶意。实验表明，该算法确实可以达到较高的分类准确率，达到99%以上。我们还开发了一种结合CVM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。我们的应用使医疗云为同一医院内部或不同医院之间的医疗数据共享带来更多的便利，对未经授权的访问更安全。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，确保只有经过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.