Reconfigurable CAN Intrusion Detection and Response System

Author:

Saini Rachit1,Islam Riadul1ORCID

Affiliation:

1. Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD 21250, USA

Abstract

The controller area network (CAN) remains the de facto standard for intra-vehicular communication. CAN enables reliable communication between various microcontrollers and vehicle devices without a central computer, which is essential for sustainable transportation systems. However, it poses some serious security threats due to the nature of communication. According to caranddriver.com, there were at least 150 automotive cybersecurity incidents in 2019, a 94% year-over-year increase since 2016, according to a report from Upstream Security. To safeguard vehicles from such attacks, securing CAN communication, which is the most relied-on in-vehicle network (IVN), should be configured with modifications. In this paper, we developed a configurable CAN communication protocol to secure CAN with a hardware prototype for rapidly prototyping attacks, intrusion detection systems, and response systems. We used a field programmable gate array (FPGA) to prototype CAN to improve reconfigurability. This project focuses on attack detection and response in the case of bus-off attacks. This paper introduces two main modules: the multiple generic errors module with the introduction of the error state machine (MGEESM) module and the bus-off attack detection (BOAD) module for a frame size of 111 bits (BOAD111), based on the CAN protocol presenting the introduction of form error, CRC error, and bit error. Our results show that, in the scenario with the transmit error counter (TEC) value 127 for switching between the error-passive state and bus-off state, the detection times for form error, CRC error, and bit error introduced in the MGEESM module are 3.610 ms, 3.550 ms, and 3.280 ms, respectively, with the introduction of error in consecutive frames. The detection time for BOAD111 module in the same scenario is 3.247 ms.

Funder

UMBC

National Science Foundation

Publisher

MDPI AG

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