PhD student profile

Xiaojie on her research

Xiaojie’s project looks into the vulnerability of vehicular network communications, exploring how vehicles are susceptible to cyber-attacks, and to propose defence methods against them. The main focus of her project is to predict the decoding specification of in-vehicle network messages. These are kept secret by Original Equipment Manufacturers but might be exploited as a potential attack method.

The number of cars under cyber-attacks increases as cars become smarter, with advanced functions and access points, such as Bluetooth and Over the Air system updates. Little research has been undertaken in this specific area. As a cybersecurity student, Xiaojie understands the importance of the work, and is excited by the opportunity to investigate this area.

“Since beginning this work I have simulated and proved cyber-attacks on real vehicles in the IAG lab. A reverse engineering system on Controller Area Network (CAN) has been designed and tested, and I am proud that we proposed the reverse engineering system in theory and have implemented in our WebApp to demonstrate its function as a Minimum Viable product.”

“I’m very fortunate to be able to work with IAG lab engineers Phuoc Nguyen and Jacob Pace. They provide not only hands-on support, but also insights on research gaps and industry demands.”

This work throws light on the increasing risk of cyber-attacks on vehicle networks, and demonstrates the perils of car hacking. Cybersecurity solutions will be designed and implemented to address security challenges in vehicular and V2X networks to protect drivers’ safety, and new ways to assess vehicle security will be proposed for car insurance purposes.

Research publications and patents will be generated throughout the project.

Reflections on her PhD

Weekly catch-ups with IAG engineers, in addition to working with my university supervisor, has been incredibly valuable to Xiaojie’s work on this PhD. These interactions and guidance have been not only enabled the exchange of information, but also to mitigate and solve technical problems.

She has also improved her teamwork and communication/presentation skills by working with industry engineers, not to mention obtaining hands-on skills in car operations.

Xiaojie plans to keep working in the vehicle cybersecurity field after her PhD. “It’s a challenging but significant topic, crucial to vehicle security and driver safety both now and in the future. I aim to highlight attention on cybersecurity issues of vehicles and provide defence solutions against advanced vehicle hacking.”

Ongoing she will continue to look for chances to investigate and solve vehicle cybersecurity issues, working with engineers in the automotive industry or academics with related research interests. She will definitely continue to be an advocate for vehicle security in whatever roles she takes on in the future.

A poster for Xiaojie’s PhD project, made for display at the 2022 ITS Australia awards.

A word from PhD supervisor, Dr Xu Wang

Why is this PhD important to investigate?

This PhD project is about vehicle security and privacy, identifying the vulnerability of vehicle security and privacy, assessing vehicle security, and proposing new designs to improve vehicle security. The project is motivated by the fact that as vehicles nowadays are more intelligent than ever before, but are fast becoming becoming open to cyberattacks.

What are the major challenges to overcome in the field?

The biggest challenge of this project are the heteronomous vehicle designs. The vehicle control systems and signals vary across makers, models, and years of manufacture. We are working on a unified security assessment framework to reduce the repetitive and tedious tests on individual vehicles. However, extensive assessments still need to be conducted to measure the security of tested vehicles.

Another challenge is about applying AI technology in vehicle security. AI technology has shown its power on image, voice, and language tasks but has not been widely adopted in vehicle security applications due to limited datasets, private vehicle signal specification, and unique AI tasks on vehicle security. We are developing AI advances to solve vehicle security and privacy issues.

Where might this work lead in the (near and far) future?

The project outcomes will enable rapid and efficient vehicle security assessment for business users, reinforce security awareness of the vehicle industry, and enhance vehicle security. They will also assist the autonomous vehicles industry and lay a secure foundation for smart and sustainable transportation systems in the long term.

Contact Xiaojie

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Publications

Lin, Xiaojie, Baihe Ma, Xu Wang, Ying He, Ren Ping Liu, and Wei Ni. Multi-layer Reverse Engineering System for Vehicular Controller Area Network Messages. In 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 1185-1190. IEEE, 2022.

Liang, L., X. Lin, B. Ma, X. Wang, Y. He, R. Liu, and W. Ni. Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks. In 2022 IEEE Global Communications Conference. 2022. (Note that this paper is accepted by 2022 IEEE GlobalCom but not published yet, it should be published officially early next year)

Ma, Baihe, Xiaojie Lin, Xu Wang, Bin Liu, Ying He, Wei Ni, and Ren Ping Liu. New Cloaking Region Obfuscation for Road Network- Indistinguishability and Location Privacy. In 25th International Symposium on Research in Attacks, Intrusions and Defenses, pp. 160-170. 2022.