Cybersecurity Vulnerabilities for Off-Board Commercial Vehicle Diagnostics

Author:

Kumar Sharika1,Daily Jeremy2,Ahmed Qadeer3,Arora Anish3

Affiliation:

1. Cummins Inc.

2. Colorado State University

3. Ohio State University

Abstract

<div class="section abstract"><div class="htmlview paragraph">The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions. A solution is proposed to mitigate these attacks by utilizing machine authentication codes or authenticated encryption with pre-shared keys between the communicating parties. Various tradeoffs, such as communication overhead encryption time and J1939 protocol compliance, are presented while implementing the mitigation strategy. One of our key findings is that the data flowing through RP1210-based diagnostic systems are vulnerable to MitM attacks launched from the host diagnostics computer. Security models should include controls to detect and mitigate these data flows. An example of a cryptographic security control to mitigate the risk of an MitM attack was implemented and demonstrated by using the SAE J1939 DM18 message. This approach, however, utilizes over twice the bandwidth as normal communications. Sensitive data should utilize such a security control.</div></div>

Publisher

SAE International

Subject

Artificial Intelligence,Mechanical Engineering,Fuel Technology,Automotive Engineering

Reference26 articles.

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3. Hoppe , T. , and Dittman , J. Sniffing/Replay Attacks on CAN Buses: A Simulated Attack on the Electric Window Lift Classified Using an Adapted CERT Taxonomy Workshop on Embedded Systems Security (WESS) 2007

4. Merco , R. , Biron , Z.A. , and Pisu , P. Replay Attack Detection in a Platoon of Connected Vehicles with Cooperative Adaptive Cruise Control 2018 Annual American Control Conference (ACC) 2018 5582 5587 https://doi.org/10.23919/ACC.2018.8431538

5. Matsubayashi , M. et al. Attacks Against UDS on DoIP by Exploiting Diagnostic Communications and Their Countermeasures IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) 1 6 2021 https://doi.org/10.1109/VTC2021-Spring51267.2021.9448963

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