In recent years, a large number of botnets and dark networks rely on command and control channels of unknown protocol formats for communication, and with the development of Internet of Things technology, this problem becomes more prominent. The syntax analysis of the unknown protocol is helpful to measure the boundary of Botnet in the environment of Internet of things, so as to protect the network security. Based on the analysis of the characteristics of the current bitstream protocol data format, this article proposes an unknown protocol syntax analysis method based on convolutional neural network (CNN). First, the protocol data are preprocessed, and then the image is transformed. Next, the converted image is input to the convolution layer for convolution. After convolution, the data are flattened. Then the flattened data are put into the fully connected neural network. Finally, the unknown protocol is analyzed and predicted. The experimental results show that compared with the traditional feature extraction combine frequent item algorithm (CFI) and other neural network deep neural networks, CNN is 15% more accurate than CFI in the analysis of unknown protocol syntax, and it can accurately analyze and identify the unknown protocol.

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Electrocatalytic nitrogen reduction reaction (NRR) is a green method for synthesis ammonia under mild condition compared to energy‐intensive Haber‐Bosch process. Herein, a novel heterojunction material (MMZ‐900) is prepared as an excellent electrocatalyst for NRR at 900 °C, which is composed of MoS2, MoO2 and ZnO with nonuniform charge distribution of rich S and O vacancies. As a result, MMZ‐900 exhibits about 8 times higher Faradaic efficiency and 5 times higher NH3 yields for NRR than pure MoS2, respectively. These new insights may open up opportunities for exploiting efficient NRR electrocatalysts by simultaneously improving sulfur and oxygen vacancies on heterojunction material.

Internet of Things (IoT) is the development and extension of computer, Internet, and mobile communication network and other related technologies, and in the new era of development, it increasingly shows its important role. To play the role of the Internet of Things, it is especially important to strengthen the network communication information security system construction, which is an important foundation for the Internet of Things business relying on Internet technology. Therefore, the communication protocol between IoT devices is a point that cannot be ignored, especially in recent years; the emergence of a large number of botnet and malicious communication has seriously threatened the communication security between connected devices. Therefore, it is necessary to identify these unknown protocols by reverse analysis. Although the development of protocol analysis technology has been quite mature, it is impossible to identify and analyze the unknown protocols of pure bitstreams with zero a priori knowledge using existing protocol analysis tools. In this paper, we make improvements to the existing protocol analysis algorithm, summarize and learn from the experience and knowledge of our predecessors, improve the algorithm ideas based on the Apriori algorithm idea, and perform feature string finding under the idea of composite features of CFI (Combined Frequent Items) algorithm. The advantages of existing algorithm ideas are combined together to finally propose a more efficient OFS (Optimal Feature Strings) algorithm with better performance in the face of bitstream protocol feature extraction problems.