Soyeon Son (Korea University) Kyungho Joo (Korea University) Wonsuk Choi (Korea University) Dong Hoon Lee (Korea University)

ETAS Best Paper Award ($500 cash prize)!

The proliferation of electric vehicles (EVs) and the simultaneous expansion of EV charging infrastructure have underscored the growing importance of securing EV charging systems. Power line communication is one of the most widely implemented communication technologies that is standardized by combined charging system (CCS) and the North American charging standard (NACS). Recently, it has been revealed that an unshielded charging cable can function as a susceptible antenna. As a result, attackers can eavesdrop on communication packets between charging stations and EVs or maliciously suspend charging sessions.

To secure EV charging systems against signal injection attack, we propose a signal cancellation system that restores benign charging sessions by annihilating the attack signal. An essential step in the proposed method is accurately estimating the carrier phase offset (CPO) and channel state values of the attack signal. Due to the inaccurate estimation of CPO and channel state values, continuous updates using linear interpolation are necessary. To evaluate the effectiveness of the proposed technique, we show that normal communication is achieved with the success of the signal level attenuation characterization (SLAC) protocol within 1.5 seconds. Experiments are conducted to determine the appropriate update parameters for attaining a 100% success rate in normal communication. We also analyze the error between the predicted CPO and channel state values and the actual CPO and channel state values of the attack signals. Furthermore, the effectiveness of the proposed method is evaluated based on the power of the injected attack signal. We have confirmed that when the power of the received attack signal is less than −31.8dBm, applying the proposed technique with the suitable update parameters leads to 100% success in normal communication.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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