Kyungho Joo (Korea University), Wonsuk Choi (Korea University), Dong Hoon Lee (Korea University)

Recently, the traditional way to unlock car doors has been replaced with a keyless entry system which proves more convenient for automobile owners. When a driver with a key fob is in vicinity of the vehicle, doors automatically unlock on user command. However, unfortunately, it has been known that these keyless entry systems are vulnerable to signal-relaying attacks. While it is evident that automobile manufacturers incorporate preventative methods to secure these keyless entry systems, a range of attacks continue to occur. Relayed signals fit into the valid packets that are verified as legitimate, and this makes it is difficult to distinguish a legitimate request for doors to be unlocked from malicious signals. In response to this vulnerability, this paper presents an RF-fingerprinting method (coined “HOld the DOoR”, HODOR) to detect attacks on keyless entry systems, which is the first attempt to exploit RF-fingerprint technique in automotive domain. HODOR is designed as a sub-authentication system that supports existing authentication systems for keyless entry systems and does not require any modification of the main system to perform. Through a series of experiments, the results demonstrate that HODOR competently and reliably detects attacks on keyless entry systems. HODOR achieves both an average false positive rate (FPR) of 0.27% with a false negative rate (FNR) of 0% for the detection of simulated attacks corresponding to the current issue on keyless entry car theft. Furthermore, HODOR was also observed under environmental factors: temperature variation, non-line-of-sight (NLoS) conditions and battery aging. HODOR yields a false positive rate of 1.32% for the identification of a legitimated key fob which is even under NLoS condition. Based on the experimental results, it is expected that HODOR will provide a secure service for keyless entry systems, while remaining convenient.

View More Papers

Measuring the Deployment of Network Censorship Filters at Global...

Ram Sundara Raman (University of Michigan), Adrian Stoll (University of Michigan), Jakub Dalek (Citizen Lab, University of Toronto), Reethika Ramesh (University of Michigan), Will Scott (Independent), Roya Ensafi (University of Michigan)

Read More

Strong Authentication without Temper-Resistant Hardware and Application to Federated...

Zhenfeng Zhang (Chinese Academy of Sciences, University of Chinese Academy of Sciences, and The Joint Academy of Blockchain Innovation), Yuchen Wang (Chinese Academy of Sciences and University of Chinese Academy of Sciences), Kang Yang (State Key Laboratory of Cryptology)

Read More

SymTCP: Eluding Stateful Deep Packet Inspection with Automated Discrepancy...

Zhongjie Wang (University of California, Riverside), Shitong Zhu (University of California, Riverside), Yue Cao (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside), Kevin S. Chan (U.S. Army Research Lab), Tracy D. Braun (U.S. Army Research Lab)

Read More

CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing Hua University), Tsung-Yi Ho (National Tsing Hua University), Yier Jin (University of Florida)

Read More