Work

Data-driven-based spatio-temporal detection model against cyber attacks in Intelligent transportation

Year: 2025

Type: article

Abstract: The escalating sophistication of cyber attacks in intelligent transportation systems necessitates the development of advanced intrusion detection systems capable of processing multi-modal data efficiently while maintaining real-time performance. To address these critical cybersecurity challenges, this paper presents a novel spatio-temporal hybrid model for robust attack detection. Our framework, designed to overcome the limitations of existing methods in handling complex hybrid threats, integrates three key components: convolutional neural networks (CNN) for spatial feature extraction; bidirectional long short-term memory (BiLSTM) networks to capture long-range temporal dependencies, and an attention mechanism for adaptive feature weighting. This innovative architecture enables comprehensive spatio-temporal pattern analysis while dynamically prioritizing the most discriminative features. Extensive experimental results demonstrate that our model significantly outperforms existing AI-based detection methods, such as Korea New Network-KNN and Multilayer Perceptron-MLP, achieving superior detection accuracy and enhanced robustness against sophisticated attacks.

Source: Measurement and Control

DOI: 10.1177/00202940251400036

Authors: Runxian Wei, Mingyue Zhang, Xinyu Wang

Institutions: Shuozhou Ceramic Institute of Technology, Yanshan University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,000