ErrIDS: An Enhanced Cumulative Timing Error-Based Automotive Intrusion Detection System

Lee, Soohyung; Choi, Wonsuk; Jo, Hyo Jin; Lee, Dong Hoon

doi:10.1109/tits.2023.3293517

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…The fingerprints are then used for constructing a baseline of the ECUs' clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification of errors, which is a clear sign of intrusion [50][51][52][53][54][55][56]. the physical fingerprints of ECUs.…”

Section: The State-of-the-art On Can Cybersecuritymentioning

confidence: 99%

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity

Dini,

Saponara

2023

Sensors

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In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm’s resilience under varying temperatures.

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Section: The State-of-the-art On Can Cybersecuritymentioning

confidence: 99%

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity

Dini,

Saponara

2023

Sensors

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DGIDS: Dynamic graph-based intrusion detection system for CAN

Song,

Qin,

Liang

et al. 2024

Computers & Security

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Intrusion detection and defence for CAN bus through frequency anomaly analysis and arbitration mechanism

Dong,

Song,

Shao

2024

Electronics Letters

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As automobile intelligence continues to develop, the electronic control units connected to the Controller Area Network (CAN) bus within vehicles face an increasing number of threats from potential attacks originating from the internet. To address this issue, an intrusion detection and defence method is proposed that is capable of detecting illegal messages through the use of frequency anomaly detection, and subsequently disrupting them through utilization of the CAN bus arbitration mechanism. This proposed method offers a simple implementation compared to traditional approaches, while being able to identify suspicious units and neutralize threats in real‐time. Experimental results demonstrate the effectiveness of this approach.

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ErrIDS: An Enhanced Cumulative Timing Error-Based Automotive Intrusion Detection System

Cited by 6 publications

References 25 publications

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity

DGIDS: Dynamic graph-based intrusion detection system for CAN

Intrusion detection and defence for CAN bus through frequency anomaly analysis and arbitration mechanism

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