Visible to the public Biblio

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Conference Paper
Liu, Jing, Lai, Yingxu, Zhang, Shixuan.  2017.  FL-GUARD: A Detection and Defense System for DDoS Attack in SDN. Proceedings of the 2017 International Conference on Cryptography, Security and Privacy. :107–111.

This paper proposed a new detection and prevention system against DDoS (Distributed Denial of Service) attack in SDN (software defined network) architecture, FL-GUARD (Floodlight-based guard system). Based on characteristics of SDN and centralized control, etc., FL-GUARD applies dynamic IP address binding to solve the problem of IP spoofing, and uses 3.3.2 C-SVM algorithm to detect attacks, and finally take advantage of the centralized control of software-defined network to issue flow tables to block attacks at the source port. The experiment results show the effectiveness of our system. The modular design of FL-GUARD lays a good foundation for the future improvement.

Zhao, Yongxin, Wu, Xi, Liu, Jing, Yang, Yilong.  2018.  Formal Modeling and Security Analysis for OpenFlow-Based Networks. 2018 23rd International Conference on Engineering of Complex Computer Systems (ICECCS). :201–204.
We present a formal OpenFlow-based network programming language (OF) including various flow rules, which can not only describe the behaviors of an individual switch, but also support to model a network of switches connected in the point-to-point topology. Besides, a topology-oriented operational semantics of the proposed language is explored to specify how the packet is processed and delivered in the OpenFlow-based networks. Based on the formal framework, we also propose an approach to detect potential security threats caused by the conflict of dynamic flow rules imposed by dynamic OpenFlow applications.
Ren, Zhengwei, Zha, Xianye, Zhang, Kai, Liu, Jing, Zhao, Heng.  2019.  Lightweight Protection of User Identity Privacy Based on Zero-knowledge Proof. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). :2549–2554.
A number of solutions have been proposed to tackle the user privacy-preserving issue. Most of existing schemes, however, focus on methodology and techniques from the perspective of data processing. In this paper, we propose a lightweight privacy-preserving scheme for user identity from the perspective of data user and applied cryptography. The basic idea is to break the association relationships between User identity and his behaviors and ensure that User can access data or services as usual while the real identity will not be revealed. To this end, an interactive zero-knowledge proof protocol of identity is executed between CSP and User. Besides, a trusted third-party is introduced to manage user information, help CSP to validate User identity and establish secure channel between CSP and User via random shared key. After passing identity validation, User can log into cloud platform as usual without changing existing business process using random temporary account and password generated by CSP and sent to User by the secure channel which can further obscure the association relationships between identity and behaviors. Formal security analysis and theoretic and experimental evaluations are conducted, showing that the proposal is efficient and practical.
Guo, Longteng, Liu, Jing, Wang, Yuhang, Luo, Zhonghua, Wen, Wei, Lu, Hanqing.  2017.  Sketch-Based Image Retrieval Using Generative Adversarial Networks. Proceedings of the 25th ACM International Conference on Multimedia. :1267–1268.

For sketch-based image retrieval (SBIR), we propose a generative adversarial network trained on a large number of sketches and their corresponding real images. To imitate human search process, we attempt to match candidate images with theimaginary image in user single s mind instead of the sketch query, i.e., not only the shape information of sketches but their possible content information are considered in SBIR. Specifically, a conditional generative adversarial network (cGAN) is employed to enrich the content information of sketches and recover the imaginary images, and two VGG-based encoders, which work on real and imaginary images respectively, are used to constrain their perceptual consistency from the view of feature representations. During SBIR, we first generate an imaginary image from a given sketch via cGAN, and then take the output of the learned encoder for imaginary images as the feature of the query sketch. Finally, we build an interactive SBIR system that shows encouraging performance.