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2022-11-25
Li, Shengyu, Meng, Fanjun, Zhang, Dashun, Liu, Qingqing, Lu, Li, Ye, Yalan.  2021.  Research on Security Defense System of Industrial Control Network. 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). 2:631—635.
The importance of the security of industrial control network has become increasingly prominent. Aiming at the defects of main security protection system in the intelligent manufacturing industrial control network, we propose a security attack risk detection and defense, and emergency processing capability synchronization technology system suitable for the intelligent manufacturing industrial control system. Integrating system control and network security theories, a flexible and reconfigurable system-wide security architecture method is proposed. On the basis of considering the high availability and strong real-time of the system, our research centers on key technologies supporting system-wide security analysis, defense strategy deployment and synchronization, including weak supervision system reinforcement and pattern matching, etc.. Our research is helpful to solve the problem of industrial control network of “old but full of loopholes” caused by the long-term closed development of the production network of important parts, and alleviate the contradiction between the high availability of the production system and the relatively backward security defense measures.
2020-03-30
Li, Jian, Zhang, Zelin, Li, Shengyu, Benton, Ryan, Huang, Yulong, Kasukurthi, Mohan Vamsi, Li, Dongqi, Lin, Jingwei, Borchert, Glen M., Tan, Shaobo et al..  2019.  Reversible Data Hiding Based Key Region Protection Method in Medical Images. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :1526–1530.
The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.