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Jiang, Xiaoyu, Qiu, Tie, Zhou, Xiaobo, Zhang, Bin, Sun, Ximin, Chi, Jiancheng.  2021.  A Text Similarity-based Protocol Parsing Scheme for Industrial Internet of Things. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :781–787.
Protocol parsing is to discern and analyze packets' transmission fields, which plays an essential role in industrial security monitoring. The existing schemes parsing industrial protocols universally have problems, such as the limited parsing protocols, poor scalability, and high preliminary information requirements. This paper proposes a text similarity-based protocol parsing scheme (TPP) to identify and parse protocols for Industrial Internet of Things. TPP works in two stages, template generation and protocol parsing. In the template generation stage, TPP extracts protocol templates from protocol data packets by the cluster center extraction algorithm. The protocol templates will update continuously with the increase of the parsing packets' protocol types and quantities. In the protocol parsing phase, the protocol data packet will match the template according to the similarity measurement rules to identify and parse the fields of protocols. The similarity measurement method comprehensively measures the similarity between messages in terms of character position, sequence, and continuity to improve protocol parsing accuracy. We have implemented TPP in a smart industrial gateway and parsed more than 30 industrial protocols, including POWERLINK, DNP3, S7comm, Modbus-TCP, etc. We evaluate the performance of TPP by comparing it with the popular protocol analysis tool Netzob. The experimental results show that the accuracy of TPP is more than 20% higher than Netzob on average in industrial protocol identification and parsing.
Liu, Zhenpeng, He, Yupeng, Wang, Wensheng, Wang, Shuo, Li, Xiaofei, Zhang, Bin.  2019.  AEH-MTD: Adaptive Moving Target Defense Scheme for SDN. 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). :142–147.

Distributed Denial of Service attack is very harmful to software-defined networking. Effective defense measures are the key to ensure SDN security. An adaptive moving target defense scheme based on end information hopping for SDN is proposed. The source address entropy value and the flow rate method are used to detect the network condition. According to the detection result, the end information is adjusted by time adaptive or space adaptive. A model of active network defense is constructed. The experimental results show that the proposed scheme enhances the anti-attack capability and serviceability compared with other methods, and has greater dynamics and flexibility.

Liu, Ying, He, Qiang, Zheng, Dequan, Zhang, Mingwei, Chen, Feifei, Zhang, Bin.  2019.  Data Caching Optimization in the Edge Computing Environment. 2019 IEEE International Conference on Web Services (ICWS). :99–106.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.