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

TL;DR

This paper introduces MDHP-Net, a novel detection method for time-exciting cyberattacks on vehicle in-vehicle networks, validated through real-world experiments and a new dataset, enhancing vehicle cybersecurity.

Contribution

We propose MDHP-Net, a new detection framework using Multi-Dimentional Hawkes Process for identifying time-exciting attacks in IVN, and release STEIA9, the first open-source dataset for such attacks.

Findings

MDHP-Net outperforms baseline detection methods.

Time-exciting attacks are feasible on Ethernet-based vehicle networks.

The new dataset STEIA9 enables further research in this area.

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.