Generative Adversarial Pipelines for Driving Data Anomaly Detection with Microservices and Containerization in AI-Driven Cybersecure Systems
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Abstract
The increasing complexity of autonomous and connected vehicle ecosystems necessitates advanced mechanisms for detecting anomalies in driving data to ensure operational safety and security. This paper presents a generative adversarial network (GAN)-based pipeline designed for real-time anomaly detection in heterogeneous vehicular data streams, including sensor readings, vehicle-to-vehicle (V2V) communications, and telemetry logs. Leveraging microservices and containerization, the framework ensures modularity, scalability, and efficient deployment across edge and cloud environments. AI-driven analytics enable proactive identification of abnormal patterns, while integrated cybersecurity mechanisms provide continuous threat monitoring and secure data handling. Experimental results demonstrate that the proposed pipeline achieves high detection accuracy, low latency, and robustness under diverse driving scenarios. The study highlights the potential of combining GANs, microservices, and AI-enhanced cybersecurity to create resilient and reliable autonomous driving systems.
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