Visible to the public Biblio

Filters: Author is Lin, Hui  [Clear All Filters]
2021-09-16
He, Hongqi, Lin, Hui, Wang, Ruimin, Wang, Huanwei.  2020.  Research on RFID Technology Security. 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). :423–427.
In recent years, the Internet of Things technology has developed rapidly. RFID technology, as an important branch of the Internet of Things technology, is widely used in logistics, medical, military and other fields. RFID technology not only brings convenience to people's production and life, but also hides many security problems. However, the current research on RFID technology mainly focuses on the technology application, and there are relatively few researches on its security analysis. This paper firstly studies the authentication mechanism and storage mechanism of RFID technology, then analyzes the common vulnerabilities of RFID, and finally gives the security protection suggestions.
2017-03-20
Amullen, Esther, Lin, Hui, Kalbarczyk, Zbigniew, Keel, Lee.  2016.  Multi-agent System for Detecting False Data Injection Attacks Against the Power Grid. Proceedings of the 2Nd Annual Industrial Control System Security Workshop. :38–44.

A class of cyber-attacks called False Data Injection attacks that target measurement data used for state estimation in the power grid are currently under study by the research community. These attacks modify sensor readings obtained from meters with the aim of misleading the control center into taking ill-advised response action. It has been shown that an attacker with knowledge of the network topology can craft an attack that bypasses existing bad data detection schemes (largely based on residual generation) employed in the power grid. We propose a multi-agent system for detecting false data injection attacks against state estimation. The multi-agent system is composed of software implemented agents created for each substation. The agents facilitate the exchange of information including measurement data and state variables among substations. We demonstrate that the information exchanged among substations, even untrusted, enables agents cooperatively detect disparities between local state variables at the substation and global state variables computed by the state estimator. We show that a false data injection attack that passes bad data detection for the entire system does not pass bad data detection for each agent.