Billions of devices are connected via the Internet which has produced various challenges and opportunities. The increase in the number of devices connected to the Internet of things (IoT) is nearly beyond imagination. These devices are communicating with each other and facilitating human life. The connection of these devices has provided opening directions for the smart applications which are one of the growing areas of research. Among these opportunities, security and privacy are considered to be one of the major issues for researchers to tackle. Proper security measures can prevent attackers from interrupting the security of IoT network inside the smart city for secure data traffic. Keeping in view the security consideration of data traffic for smart devices and IoT, the proposed study presented machine learning algorithms for securing the data traffic based on a firewall for smart devices and IoT network. The study has used the dataset of “Firewall” for validation purposes. The experimental results of the approach show that the hybrid deep learning model (based on convolution neural network and support vector machine) outperforms than decision1 rules and random forest by generating a recognition rate of 95.5% for the hybrid model, 68.5% for decision rules, and 78.3% accuracy for random forest. The validity of the proposed model is also tested based on other performance metrics such as f score, error rate, recall, and precision. This high accuracy rate and other performance values show the applicability of the proposed hybrid model to secure data traffic purposes in smart devices. This can be used in many research areas of the smart city for security purposes.

中文翻译：

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使用机器学习算法获取可持续物联网和智能设备的数据流量

数十亿台设备通过互联网连接，这带来了各种挑战和机遇。连接到物联网 (IoT) 的设备数量的增加几乎超乎想象。这些设备相互通信并促进人类生活。这些设备的连接为智能应用提供了开放的方向，智能应用是不断增长的研究领域之一。在这些机会中，安全和隐私被认为是研究人员要解决的主要问题之一。适当的安全措施可以防止攻击者为了安全数据流量而中断智慧城市内部物联网网络的安全。考虑到智能设备和物联网数据流量的安全考虑，拟议的研究提出了基于智能设备和物联网网络的防火墙来保护数据流量的机器学习算法。该研究使用“防火墙”数据集进行验证。该方法的实验结果表明，混合深度学习模型（基于卷积神经网络和支持向量机）的性能优于决策1规则和随机森林，混合模型的识别率为95.5%，决策规则为68.5%，和 78.3% 的随机森林准确率。所提出模型的有效性还基于其他性能指标（如 f 分数、错误率、召回率和精度）进行了测试。这种高准确率和其他性能值表明所提出的混合模型适用于保护智能设备中的数据流量目的。