Next Article in Journal
Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor
Previous Article in Journal
Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
Previous Article in Special Issue
Threshold Cryptography-Based Secure Vehicle-to-Everything (V2X) Communication in 5G-Enabled Intelligent Transportation Systems
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Xingyu Li
*,
Ruifeng Li
and
Yanchen Liu
The School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Future Internet 2024, 16(6), 185; https://doi.org/10.3390/fi16060185
Submission received: 17 April 2024 / Revised: 7 May 2024 / Accepted: 21 May 2024 / Published: 23 May 2024
(This article belongs to the Special Issue Security for Vehicular Ad Hoc Networks)

Abstract

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.
Keywords: Hawkes process; LSTM; DDoS; SOME/IP Hawkes process; LSTM; DDoS; SOME/IP

Share and Cite

MDPI and ACS Style

Li, X.; Li, R.; Liu, Y. HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network. Future Internet 2024, 16, 185. https://doi.org/10.3390/fi16060185

AMA Style

Li X, Li R, Liu Y. HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network. Future Internet. 2024; 16(6):185. https://doi.org/10.3390/fi16060185

Chicago/Turabian Style

Li, Xingyu, Ruifeng Li, and Yanchen Liu. 2024. "HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network" Future Internet 16, no. 6: 185. https://doi.org/10.3390/fi16060185

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop