Gaetano Coppoletta (University of Illinois Chicago), Rigel Gjomemo (Discovery Partners Institute, University of Illinois), Amanjot Kaur, Nima Valizadeh (Cardiff University), Venkat Venkatakrishnan (Discovery Partners Institute, University of Illinois), Omer Rana (Cardiff University)

In the last decade, electric vehicles (EVs) have moved from a niche of the transportation sector to its most innovative, dynamic, and growing sector. The associated EV charging infrastructure is closely following behind. One of the main components of such infrastructure is the Open Charge Point Protocol (OCPP), which defines the messages exchanged between charging stations and central management systems owned by charging companies. This paper presents OCPPStorm, a tool for testing the security of OCPP implementations. OCPPStorm is designed as a black box testing tool, in order to be able to deal with different implementations, independently of their deployment peculiarities, platforms, or languages used. In particular, OCPPStorm applies fuzzing techniques to the OCPP messages to identify errors in the message management and find vulnerabilities among those errors. It’s efficacy is demonstrated through extensive testing on two open-source OCPP systems, revealing its proficiency in uncovering critical security flaws, among which 5 confirmed CVEs and 7 under review. OCPPSTorm’s goal is to bolster the methodological approach to OCPP security testing, thereby reinforcing the reliability and safety of the EV charging ecosystem.

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Lancheng Qin (Tsinghua University, BNRist), Li Chen (Zhongguancun Laboratory), Dan Li (Tsinghua University, Zhongguancun Laboratory), Honglin Ye (Tsinghua University), Yutian Wang (Tsinghua University)

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Michael Westra (In-Vehicle Cyber Security Technical Manager, Ford)

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

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GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity

Qiushi Wu (University of Minnesota), Zhongshu Gu (IBM Research), Hani Jamjoom (IBM Research), Kangjie Lu (University of Minnesota)

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