Timing Predictability for SOME/IP-based Service-Oriented Automotive In-Vehicle Networks

Fraccaroli, Enrico; Joshi, Prachi; Xu, Shengjie; Shazzad, Khaja Mohammad; Jochim, Markus; Chakraborty, Samarjit

doi:10.23919/date56975.2023.10137065

Cited by 7 publications

(2 citation statements)

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“…In the Hawkes Process Formulation Module, we assume that each ECU in the vehicle corresponds to a unique IP address in network communications [38,39]. We model the IP addresses within each time window using the Hawkes process and apply the maximum likelihood estimation method to estimate the Hawkes parameters for each IP address within each time window according to Equation (11). These parameters, along with the preprocessed data, are then fed into the Training Module for the HP-LSTM model to learn.…”

Section: Overview Of Our Modelmentioning

confidence: 99%

“…The increasing number of ECUs and the development of infotainment systems have led to the emergence of in-vehicle Ethernet with ultra-high bandwidth [9,10]. The main driving forces behind this trend are the growing demand for computational and communication bandwidth, as well as the need for flexibility in adding software [11]. In this case, the SOME/IP, unique for its communication mechanism, has seen rapid development since being proposed by BMW in 2011.…”

Section: Introductionmentioning

confidence: 99%

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HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network

Li,

Liu

2024

Future Internet

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Connected and autonomous vehicles (CAVs) are advancing at a fast speed with the improvement of the automotive industry, which opens up new possibilities for different attacks. A Distributed Denial-of-Service (DDoS) attacker floods the in-vehicle network with fake messages, resulting in the failure of driving assistance systems and impairment of vehicle control functionalities, seriously disrupting the normal operation of the vehicle. In this paper, we propose a novel DDoS attack detection method for in-vehicle Ethernet Scalable service-Oriented Middleware over IP (SOME/IP), which integrates the Hawkes process with Long Short-Term Memory networks (LSTMs) to capture the dynamic behavioral features of the attacker. Specifically, we employ the Hawkes process to capture features of the DDoS attack, with its parameters reflecting the dynamism and self-exciting properties of the attack events. Subsequently, we propose a novel deep learning network structure, an HP-LSTM block, inspired by the Hawkes process, while employing a residual attention block to enhance the model’s detection efficiency and accuracy. Additionally, due to the scarcity of publicly available datasets for SOME/IP, we employed a mature SOME/IP generator to create a dataset for evaluating the validity of the proposed detection model. Finally, extensive experiments were conducted to demonstrate the effectiveness of the proposed DDoS attack detection method.

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Section: Overview Of Our Modelmentioning

confidence: 99%