Fanxin Kong

Assistant Professor

Electrical Engineering and Computer Science

4-295 CST

fkong03@syr.edu

(315) 443-4457

Degrees

  • Ph.D. in Computer Science, McGill University
  • M.S. in Computer Science, Northeastern University (CN)
  • B.S. in Computer Science, Northeastern University (CN)

Areas of Expertise:

  • Cyber-Physical Systems
  • Security
  • Real-time Systems
  • Resource Management

Dr. Fanxin Kong’s current research interest centers around Cyber-Physical System (CPS) security. Compared to conventional IT systems, challenges in CPS security are distinct in terms of not only consequences in case of security breaches but also attack surfaces. His recent works address real-time sensor attack detection and real-time recovery from attacks. These works are motivated by the fact that the timing correctness of attack defense receives considerably less attention than its functional correctness, but untimely defense is just damaging. The proposed solutions cover formal methods and machine learning techniques. 

Recent Publications:

  • Lin Zhang, ZifanWang, Mengyu Liu, and Fanxin Kong, “Adaptive Window-Based Sensor Attack Detection for Cyber-Physical Systems”, in the 59th Design Automation Conference (DAC), 2022.
  • Lin Zhang, Pengyuan Lu, Fanxin Kong, Xin Chen, Oleg Sokolsky and Insup Lee, “Real-Time Attack-Recovery for Cyber-Physical Systems using Linear-Quadratic Regulator”, in the 21st ACM SIGBED International Conference on Embedded Software (EMSOFT), 2021.
  • Francis Akowuah and Fanxin Kong, “Real-Time Adaptive Sensor Attack Detection in Autonomous Cyber-Physical Systems”, in 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2021.
  • Lin Zhang, Xin Chen, Fanxin Kong, and Alvaro A. Cardenas, “Real-Time Recovery for Cyber-Physical Systems using Linear Approximations”, in 41st IEEE Real-Time Systems Symposium (RTSS), 2020.
  • Tianjia He, Lin Zhang, Fanxin Kong, and Asif Salekin, “Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection”, in 57th Design Automation Conference (DAC), 2020.