Visible to the public Biblio

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Abbas, H., Suguri, H., Yan, Z., Allen, W., Hei, X. S..  2020.  IEEE Access Special Section: Security Analytics and Intelligence for Cyber Physical Systems. IEEE Access. 8:208195—208198.

A Cyber Physical System (CPS) is a smart network system with actuators, embedded sensors, and processors to interact with the physical world by guaranteeing the performance and supporting real-time operations of safety critical applications. These systems drive innovation and are a source of competitive advantage in today’s challenging world. By observing the behavior of physical processes and activating actions, CPS can alter its behavior to make the physical environment perform better and more accurately. By definition, CPS basically has two major components including cyber systems and physical processes. Examples of CPS include autonomous transportation systems, robotics systems, medical monitoring, automatic pilot avionics, and smart grids. Advances in CPS will empower scalability, capability, usability, and adaptability, which will go beyond the simple systems of today. At the same time, CPS has also increased cybersecurity risks and attack surfaces. Cyber attackers can harm such systems from multiple sources while hiding their identities. As a result of sophisticated threat matrices, insufficient knowledge about threat patterns, and industrial network automation, CPS has become extremely insecure. Since such infrastructure is networked, attacks can be prompted easily without much human participation from remote locations, thereby making CPS more vulnerable to sophisticated cyber-attacks. In turn, large-scale data centers managing a huge volume of CPS data become vulnerable to cyber-attacks. To secure CPS, the role of security analytics and intelligence is significant. It brings together huge amounts of data to create threat patterns, which can be used to prevent cyber-attacks in a timely fashion. The primary objective of this Special Section in IEEE A CCESS is to collect a complementary and diverse set of articles, which demonstrate up-to-date information and innovative developments in the domain of security analytics and intelligence for CPS.

Yan, Z., Park, Y., Leau, Y., Ren-Ting, L., Hassan, R..  2020.  Hybrid Network Mobility Support in Named Data Networking. 2020 International Conference on Information Networking (ICOIN). :16–19.
Named Data Networking (NDN) is a promising Internet architecture which is expected to solve some problems (e.g., security, mobility) of the current TCP/IP architecture. The basic concept of NDN is to use named data for routing instead of using location addresses like IP address. NDN natively supports consumer mobility, but producer mobility is still a challenge and there have been quite a few researches. Considering the Internet connection such as public transport vehicles, network mobility support in NDN is important, but it is still a challenge. That is the reason that this paper proposes an efficient network mobility support scheme in NDN in terms of signaling protocols and data retrieval.
Xu, W., Yan, Z., Tian, Y., Cui, Y., Lin, J..  2017.  Detection with compressive measurements corrupted by sparse errors. 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP). :1–5.

Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquist-rate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detection is proposed, which performs detection directly in compressive domain without reconstruction. Different from existing work that generally considers the measurements corrupted by dense noises, this paper studies the compressive detection problem when the measurements are corrupted by both dense noises and sparse errors. The sparse errors exist in many practical systems, such as the ones affected by impulse noise or narrowband interference. We derive the theoretical performance of compressive detection when the sparse error is either deterministic or random. The theoretical results are further verified by simulations.

Shuo, Y., Weimin, W., Zhiwei, K., Hua, F., Yan, Z..  2017.  Smart grid data privacy protection algorithm. 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). :242–246.

Smart grid personalized service to improve the accuracy of the grid network query, along with the data security issues worthy of our thinking. How to solve the privacy problem in the smart grid, which is a challenge to the smart grid. As data in the grid becomes more and more important, better algorithms are needed to protect the data. In this paper, we first summarize the influence of k-anonymous algorithm on sensitive attributes in standard identifiers, and then analyze the improved L-diversity algorithm from the perspective of anonymous data privacy and security. Experiments show that the algorithm can protect the data in the smart grid.