Visible to the public Biblio

Filters: Author is Zhang, Qin  [Clear All Filters]
Zhang, Xun, Zhao, Jinxiong, Yang, Fan, Zhang, Qin, Li, Zhiru, Gong, Bo, Zhi, Yong, Zhang, Xuejun.  2019.  An Automated Composite Scanning Tool with Multiple Vulnerabilities. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). :1060–1064.
In order to effectively do network security protection, detecting system vulnerabilities becomes an indispensable process. Here, the vulnerability detection module with three functions is assembled into a device, and a composite detection tool with multiple functions is proposed to deal with some frequent vulnerabilities. The tool includes a total of three types of vulnerability detection, including cross-site scripting attacks, SQL injection, and directory traversal. First, let's first introduce the principle of each type of vulnerability; then, introduce the detection method of each type of vulnerability; finally, detail the defenses of each type of vulnerability. The benefits are: first, the cost of manual testing is eliminated; second, the work efficiency is greatly improved; and third, the network is safely operated in the first time.
Yu, Ye, Belazzougui, Djamal, Qian, Chen, Zhang, Qin.  2017.  A Fast, Small, and Dynamic Forwarding Information Base. Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. :41–42.
Concise is a Forwarding information base (FIB) design that uses very little memory to support fast query of a large number of dynamic network names or flow IDs. Concise makes use of minimal perfect hashing and the SDN framework to design and implement the data structure, protocols, and system. Experimental results show that Concise uses significantly smaller memory to achieve faster query speed compared to existing FIB solutions and it can be updated very efficiently.
Chen, Di, Zhang, Qin.  2016.  Streaming Algorithms for Robust Distinct Elements. Proceedings of the 2016 International Conference on Management of Data. :1433–1447.

We study the problem of estimating distinct elements in the data stream model, which has a central role in traffic monitoring, query optimization, data mining and data integration. Different from all previous work, we study the problem in the noisy data setting, where two different looking items in the stream may reference the same entity (determined by a distance function and a threshold value), and the goal is to estimate the number of distinct entities in the stream. In this paper, we formalize the problem of robust distinct elements, and develop space and time-efficient streaming algorithms for datasets in the Euclidean space, using a novel technique we call bucket sampling. We also extend our algorithmic framework to other metric spaces by establishing a connection between bucket sampling and the theory of locality sensitive hashing. Moreover, we formally prove that our algorithms are still effective under small distinct elements ambiguity. Our experiments demonstrate the practicality of our algorithms.