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MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN

16 April 2025
Qi Liu
Yunxing Liu
Ruifeng Li
Chenhong Cao
Yangfu Li
Xuzhao Li
Peng Wang
Runhan Feng
Shiyang Bu
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Abstract

The integration of intelligent and connected technologies in modern vehicles, while offering enhanced functionalities through Electronic Control Unit (ECU) and interfaces like OBD-II and telematics, also exposes the vehicle's in-vehicle network (IVN) to potential cyberattacks. Unlike prior work, we identify a new time-exciting threat model against IVN. These attacks inject malicious messages that exhibit a time-exciting effect, gradually manipulating network traffic to disrupt vehicle operations and compromise safety-critical functions. We systematically analyze the characteristics of the threat: dynamism, time-exciting impact, and low prior knowledge dependency. To validate its practicality, we replicate the attack on a real Advanced Driver Assistance System via Controller Area Network (CAN), exploiting Unified Diagnostic Service vulnerabilities and proposing four attack strategies. While CAN's integrity checks mitigate attacks, Ethernet migration (e.g., DoIP/SOME/IP) introduces new surfaces. We further investigate the feasibility of time-exciting threat under SOME/IP. To detect time-exciting threat, we introduce MDHP-Net, leveraging Multi-Dimentional Hawkes Process (MDHP) and temporal and message-wise feature extracting structures. Meanwhile, to estimate MDHP parameters, we developed the first GPU-optimized gradient descent solver for MDHP (MDHP-GDS). These modules significantly improves the detection rate under time-exciting attacks in multi-ECU IVN system. To address data scarcity, we release STEIA9, the first open-source dataset for time-exciting attacks, covering 9 Ethernet-based attack scenarios. Extensive experiments on STEIA9 (9 attack scenarios) show MDHP-Net outperforms 3 baselines, confirming attack feasibility and detection efficacy.

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@article{liu2025_2504.11867,
  title={ MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN },
  author={ Qi Liu and Yanchen Liu and Ruifeng Li and Chenhong Cao and Yufeng Li and Xingyu Li and Peng Wang and Runhan Feng and Shiyang Bu },
  journal={arXiv preprint arXiv:2504.11867},
  year={ 2025 }
}
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