Article: Intelligent prediction of internet of vehicles (IoV) data security performance based on GF-Net Journal: International Journal of System Control and Information Processing (IJSCIP) 2026 Vol.5 No.1 pp.79 - 93 Abstract: The Internet of Vehicles (IoV) enables seamless communication. However, its complex environment creates significant data security challenges. This paper proposes a novel method to predict IoV data security performance based on GF-Net. First, an IoV secure communication model is developed over N-Nakagami channels. This model analyses security performance and generates a communication dataset. The proposed GF-Net features a dual-branch structure. The upper branch uses a GLSTM network to capture spatio-temporal features. The lower branch applies a Fusion Module to improve feature robustness. Next, a Multilayer Perceptron integrates the branch outputs for accurate prediction. Experimental results show that GF-Net outperforms the WNN, GAT, and Transformer algorithms. Notably, its Mean Squared Error is 69% lower than that of the Transformer. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent prediction of internet of vehicles (IoV) data security performance based on GF-Net

Authors: Tiancheng Li; Liliang Zhang; Lingwei Xu; T. Aaron Gulliver

Addresses: Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China ' Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China ' Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China ' Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, V8W 2Y2, Canada

Abstract: The Internet of Vehicles (IoV) enables seamless communication. However, its complex environment creates significant data security challenges. This paper proposes a novel method to predict IoV data security performance based on GF-Net. First, an IoV secure communication model is developed over N-Nakagami channels. This model analyses security performance and generates a communication dataset. The proposed GF-Net features a dual-branch structure. The upper branch uses a GLSTM network to capture spatio-temporal features. The lower branch applies a Fusion Module to improve feature robustness. Next, a Multilayer Perceptron integrates the branch outputs for accurate prediction. Experimental results show that GF-Net outperforms the WNN, GAT, and Transformer algorithms. Notably, its Mean Squared Error is 69% lower than that of the Transformer.

Keywords: IoV; Internet of Vehicles; secure communication; security performance; intelligent prediction; GF-Net.

DOI: 10.1504/IJSCIP.2026.154736

International Journal of System Control and Information Processing, 2026 Vol.5 No.1, pp.79 - 93

Received: 27 Feb 2025
Accepted: 29 Sep 2025
Published online: 12 Jul 2026 *

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Title: Intelligent prediction of internet of vehicles (IoV) data security performance based on GF-Net

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