Towards Quantitative Cybersecurity Risk Assessment in Transportation Infrastructure
PI: Prof. Song Han
Project Summary
Along with the emergence of Intelligent Transportation Systems (ITS), cybersecurity risk management in transportation infrastructure becomes more challenging. This is mainly because it is a cyber-physical system where cyber systems and components are used to control physical systems and processes, and impact human individuals (motorists, riders, etc.) as well. This project aims to take a sequence of steps to design a systematic and quantitative framework for cybersecurity risk management in representative transportation infrastructures. It will enhance the transportation infrastructure durability by i) helping identify primary assets and attack goals in representative transportation system use case; ii) providing quantitative estimation on major attack trajectories; and iii) helping identify the consequences of assets being compromised and assess the impact of these consequences.
Project Publications
[ICPS’20] Areej Althubaity, Tao Gong, Kim-Kwang Raymond Choo, Mark Nixon, Reda Ammar, Song Han, “Specification-based Distributed Detection of Rank-related Attacks in RPL-based Resource-Constrained Real-Time Wireless Networks”, in the 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2020).
[ISSPIT’20] Althubaity, Areej, Reda Ammar, and Song Han. “Detecting Rules-related Attacks in RPL-based Resource-Constrained Wireless Networks”, in IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1-8. IEEE, 2020.
[REUNS’19] Kevin Kerliu, Alexandra Ross, Tao Gong, Zelin Yun, Zhijie Shi, Song Han and Shengli Zhou, “Secure Over-The-Air Firmware Updates for Sensor Networks”, in the 6th National Workshop for REU Research in Networking and Systems (REUNS), co-located with 16th IEEE International Conference on Mobile Ad-hoc Sensor Systems (MASS), 2019.
[ETFA’17] Areej Althubaity, Huayi Ji, Tao Gong, Mark Nixon, Reda Ammar, Song Han, “ARM: A Hybrid Specification-based Intrusion Detection System for Rank Attacks in 6TiSCH Networks“, in the 22nd IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), pp.1-8, 2017.
Graduate Students
Gang Wang (UConn), Areej Althubaity (UConn), Peng Wu (UConn), Zelin Yun (UConn)
Classes related to the project
Spring/Fall 2022: SE5402 Architecture of IoT (UConn)