Enhancing Automotive Security with a Hybrid Approach towards Universal Intrusion Detection System

2025-10-07

AI Summary

To address the challenges of cross-vehicle model transferability and model degradation caused by firmware updates in automotive intrusion detection systems (IDS), this paper proposes a universal, vehicle-agnostic IDS capable of autonomously adapting to firmware evolution. Methodologically, multi-source in-vehicle signals are first transformed into the frequency domain via wavelet transform to construct a dual-frequency dataset; subsequently, interpretable statistical features are extracted by integrating Pearson correlation analysis with an independent rule system, while deep learning models jointly perform end-to-end feature learning and classification. This work introduces the first hybrid detection framework that synergistically combines statistical robustness with deep representation capability. Evaluated on four real-world vehicle datasets, the proposed method achieves significantly higher accuracy than eight state-of-the-art vehicle-specific IDSs, three general-purpose baselines, and five conventional approaches, demonstrating strong generalization and cross-platform deployability.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System (IDS) is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data generated by vehicles regarding their model, driving style, test environment, and firmware update. To address this issue, a universal IDS has been developed that can be applied to all types of vehicles without the need for customization. Unlike conventional IDSs, the universal IDS can adapt to evolving data security issues resulting from firmware updates. In this study, a new hybrid approach has been developed, combining Pearson correlation with deep learning techniques. This approach has been tested using data obtained from four distinct mechanical and electronic vehicles, including Tesla, Sonata, and two Kia models. The data has been combined into two frequency datasets, and wavelet transformation has been employed to convert them into the frequency domain, enhancing generalizability. Additionally, a statistical method based on independent rule-based systems using Pearson correlation has been utilized to improve system performance. The system has been compared with eight different IDSs, three of which utilize the universal approach, while the remaining five are based on conventional techniques. The accuracy of each system has been evaluated through benchmarking, and the results demonstrate that the hybrid system effectively detects intrusions in various vehicle models.

Problem

• Developing universal intrusion detection for diverse vehicle models
• Adapting security systems to firmware updates and evolving threats
• Combining statistical correlation with deep learning for hybrid detection

Innovation

• Hybrid approach combining Pearson correlation with deep learning
• Universal IDS adaptable to all vehicle types without customization
• Wavelet transformation converts data into frequency domain for generalizability