Industrial control protocol is the basis of communication and interaction of industrial control system, and its security is related to the whole industrial infrastructure. Because many industrial control systems use proprietary protocols, it is necessary to adopt protocol reverse analysis technology to parse them and then detect whether there are secure vulnerabilities in the protocols by means of fuzzy testing. However, most of the existing technologies are designed for common network protocols, and there is no improvement for industrial control protocol. Therefore, we propose a multistage ensemble reverse analysis method, namely, MSERA, which fully considers the design concept of industrial control protocols. MSERA divides the traditional reverse analysis process into three stages and identifies the fields with different semantic characteristics in different stages and combines with field rectification to effectively improve the results of reverse analysis of industrial control protocols. Through the experimental comparison of some public and proprietary industrial control protocols, it is found that MSERA not only outperforms Netzob in the accuracy of field split but also far exceeds Netzob in semantic recognition accuracy. The experimental results show that MSERA is very practical and suitable for reverse analysis of industrial control protocols.

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.