The rapid growth of Internet of Things (IoT) applications depends on the deployment of numerous heterogeneous devices, and the deployed devices require various communication protocols to be accessed. Matching the correct protocol for accessed devices, particularly those with unknown protocols, is a complex and challenging task due to the diversity of device types, the growing number of protocols, and the reliance on domain-specific knowledge. To address these challenges, we propose a Device Clustering and Deep Reinforcement Learning-based Protocol Generation Model (DCDPM). The DCDPM generates the best-matched protocol for protocol-unknown IoT devices using only device basic information (DBI). The DCDPM employs a two-stage device clustering mechanism based on DBI similarity density to generate device clusters, and extracts protocol features from these clusters. Furthermore, a Weight Twin Delay-DDPG (WTD-DDPG), an enhanced deep reinforcement learning (DRL) method, is developed to determine the optimal weights for identifying the optimal device cluster. The WTD-DDPG addresses issues related to continuous action space and Q-value overestimation. Lastly, a feature-original fusion mechanism is designed to further enhance protocol matching by fusing the extracted protocol features with the original protocols within the optimal device cluster. Experimental validation of the DCDPM is conducted within two distinct scenarios: a communication base station and a copper smelting production line. A device library containing 1296 devices is created and 130 devices are tested. Experimental results demonstrate that DCDPM outperforms existing methods in terms of protocol matching rate, hit rate, and network traffic consumption.

中文翻译：

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协议未知 IoT 设备的协议生成模型


物联网 （IoT） 应用的快速增长取决于众多异构设备的部署，而部署的设备需要访问各种通信协议。由于设备类型的多样性、协议数量的增加以及对特定领域知识的依赖，为访问的设备（尤其是那些具有未知协议的设备）匹配正确的协议是一项复杂且具有挑战性的任务。为了应对这些挑战，我们提出了一种基于设备集群和深度强化学习的协议生成模型 （DCDPM）。DCDPM 仅使用设备基本信息 （DBI） 为协议未知的 IoT 设备生成最佳匹配协议。DCDPM 采用基于 DBI 相似密度的两阶段设备集群机制来生成设备集群，并从这些集群中提取协议特征。此外，还开发了一种增强的深度强化学习 （DRL） 方法权重孪生延迟 DDPG （WTD-DDPG），以确定识别最佳设备集群的最佳权重。WTD-DDPG 解决了与连续动作空间和 Q 值高估相关的问题。最后，设计了一种特征-原始融合机制，通过将提取的协议特征与最优设备集群中的原始协议融合，进一步增强协议匹配。DCDPM 的实验验证在两个不同的场景中进行：通信基站和铜冶炼生产线。创建包含 1296 个设备的设备库并测试 130 个设备。实验结果表明，DCDPM 在协议匹配率、命中率和网络流量消耗方面都优于现有方法。