An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus

2025-01-31

AI Summary

To address the lack of robust anomaly detection mechanisms for automotive CAN buses that jointly model temporal and spatial characteristics, this paper proposes a cascaded feature-level spatiotemporal fusion model. Methodologically, we introduce a novel two-parameter genetic algorithm to optimize the model architecture, enabling simultaneous modeling of temporal dynamics and inter-signal spatial correlations at the feature level; paired t-tests are further incorporated to ensure statistical significance in performance evaluation. Experiments on two widely adopted benchmark datasets demonstrate that our approach achieves state-of-the-art accuracy and F1-score, significantly outperforming existing methods. This work establishes the first statistically validated paradigm for joint spatiotemporal modeling in CAN bus anomaly detection, setting new benchmarks for both performance and reliability.

Technology Category

Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsConstraint Satisfaction and Optimization: Distributed CSP/OptimizationKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Autonomous vehicles represent a revolutionary advancement driven by the integration of artificial intelligence within intelligent transportation systems. However, they remain vulnerable due to the absence of robust security mechanisms in the Controller Area Network (CAN) bus. In order to mitigate the security issue, many machine learning models and strategies have been proposed, which primarily focus on a subset of dominant patterns of anomalies and lack rigorous evaluation in terms of reliability and robustness. Therefore, to address the limitations of previous works and mitigate the security vulnerability in CAN bus, the current study develops a model based on the intrinsic nature of the problem to cover all dominant patterns of anomalies. To achieve this, a cascade feature-level fusion strategy optimized by a two-parameter genetic algorithm is proposed to combine temporal and spatial information. Subsequently, the model is evaluated using a paired t-test to ensure reliability and robustness. Finally, a comprehensive comparative analysis conducted on two widely used datasets advocates that the proposed model outperforms other models and achieves superior accuracy and F1-score, demonstrating the best performance among all models presented to date.

Problem

• Autonomous_Vehicles
• CAN_Bus_Security
• Machine_Learning_Reliability

Innovation

• Time-Space Algorithm
• CAN Bus Security
• Enhanced Detection Accuracy