Recently, biometric-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT) based security framework. The iris trait solves a lot of security issues, especially in smart IoT-based applications. It increases the resistance of these systems against severe authentication attacks. In this paper, an efficient iris recognition model based on chaotic encryption and deep Convolutional Neural Networks (CNNs) is proposed for C-IoT applications. CNN is used to extract the deep iris features from the left and right eyes, which will be used as input features to a fully connected neural network with a Softmax classifier. CASIA V4 Interval dataset and Phoenix dataset are used to train the CNN model; to get the best tuning of network parameters. In this paper, the effect of adding different kinds of noise to iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by system users, or other system assaults, is discussed. This strategy of noisy encrypted iris images is evaluated over the internet environment. Chaotic encryption is utilized to secure the transmission of iris templates in the proposed model. The results showed that the proposed approach attains supreme accuracy compared to the existing approaches, it is obtained up to 99.24% and 100% with CASIA V4 and Phoenix datasets, respectively. The proposed model achieves satisfied and competitive results regard accuracy, and robustness among existing methods. Regards to recognition accuracy rate, this methodology shows low degradation of recognition accuracy rates in the case of using noised iris images. Likewise, the proposed method has a relatively low training time, which is a useful parameter in critical IoT based uses such as Tele-Medicine application.

最近，基于生物特征的安全性在基于认知物联网（C-IoT）的安全框架的成功中起着至关重要的作用。虹膜特性解决了许多安全问题，尤其是在基于智能物联网的应用程序中。它增加了这些系统抵御严重身份验证攻击的能力。本文针对C-IoT应用，提出了一种基于混沌加密和深度卷积神经网络（CNN）的有效虹膜识别模型。CNN用于从左眼和右眼提取深虹膜特征，将其用作具有Softmax分类器的完全连接的神经网络的输入特征。CASIA V4时间间隔数据集和Phoenix数据集用于训练CNN模型；以获得最佳的网络参数调整。在本文中，将不同类型的噪声添加到虹膜图像中的效果，由于与感测IoT设备相关的噪声干扰，因此讨论了系统用户对虹膜图像的不良采集或其他系统攻击。嘈杂的加密虹膜图像的这种策略是通过Internet环境进行评估的。在所提出的模型中，利用混沌加密来确保虹膜模板的传输安全。结果表明，与现有方法相比，该方法具有最高的准确性，在CASIA V4和Phoenix数据集中，该方法分别可达到99.24％和100％。提出的模型在现有方法中的准确性和鲁棒性方面取得了令人满意的竞争结果。关于识别准确率，该方法在使用噪点虹膜图像的情况下显示出较低的识别准确率。同样地，