Visible to the public Biblio

Filters: Author is Guo, Jun  [Clear All Filters]
2020-06-01
Ye, Yu, Guo, Jun, Xu, Xunjian, Li, Qinpu, Liu, Hong, Di, Yuelun.  2019.  High-risk Problem of Penetration Testing of Power Grid Rainstorm Disaster Artificial Intelligence Prediction System and Its Countermeasures. 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2). :2675–2680.
System penetration testing is an important measure of discovering information system security issues. This paper summarizes and analyzes the high-risk problems found in the penetration testing of the artificial storm prediction system for power grid storm disasters from four aspects: application security, middleware security, host security and network security. In particular, in order to overcome the blindness of PGRDAIPS current SQL injection penetration test, this paper proposes a SQL blind bug based on improved second-order fragmentation reorganization. By modeling the SQL injection attack behavior and comparing the SQL injection vulnerability test in PGRDAIPS, this method can effectively reduce the blindness of SQL injection penetration test and improve its accuracy. With the prevalence of ubiquitous power internet of things, the electric power information system security defense work has to be taken seriously. This paper can not only guide the design, development and maintenance of disaster prediction information systems, but also provide security for the Energy Internet disaster safety and power meteorological service technology support.
2020-03-02
Wang, Qing, Wang, Zengfu, Guo, Jun, Tahchi, Elias, Wang, Xinyu, Moran, Bill, Zukerman, Moshe.  2019.  Path Planning of Submarine Cables. 2019 21st International Conference on Transparent Optical Networks (ICTON). :1–4.
Submarine optical-fiber cables are key components in the conveying of Internet data, and their failures have costly consequences. Currently, there are over a million km of such cables empowering the Internet. To carry the ever-growing Internet traffic, additional 100,000s of km of cables will be needed in the next few years. At an average cost of \$28,000 per km, this entails investments of billions of dollars. In current industry practice, cable paths are planned manually by experts. This paper surveys our recent work on cable path planning algorithms, where we use several methods to plan cable paths taking account of a range of cable risk factors in addition to cable costs. Two methods, namely, the fast marching method (FMM) and the Dijkstra's algorithm are applied here to long-haul cable path design in a new geographical region. A specific example is given to demonstrate the benefit of the FMM-based method in terms of the better path planning solutions over the Dijkstra's algorithm.