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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1. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.
2. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data. Indian Journal of Science and Technology 9 (48):1-5.
3. Gandhi, S. T. (2023). RAG-Driven Cybersecurity Intelligence: Leveraging Semantic Search for Improved Threat Detection. International Journal of Research and Applied Innovations, 6(3), 8889-8897.
4. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.
5. Sakurada, M., & Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. Proceedings of the MLSDA, 4–11.
6. Arulraj AM, Sugumar, R., Estimating social distance in public places for COVID-19 protocol using region CNN, Indonesian Journal of Electrical Engineering and Computer Science, 30(1), pp.414-424, April 2023.
7. Zhou, C., & Paffenroth, R. C. (2017). Anomaly detection with robust deep autoencoders. KDD, 665–674.
8. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative adversarial nets. NeurIPS, 27, 2672–2680.
9. Schlegl, T., Seeböck, P., Waldstein, S. M., et al. (2017). AnoGAN: Deep anomaly detection using generative adversarial networks. Medical Image Analysis, 54, 30–44.
10. Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2018). GANomaly: Semi-supervised anomaly detection via adversarial training. Asian Conference on Computer Vision.
11. Sangannagari, S. R. (2023). Smart Roofing Decisions: An AI-Based Recommender System Integrated into RoofNav. International Journal of Humanities and Information Technology, 5(02), 8-16.
12. Cherukuri, Bangar Raju. "Microservices and containerization: Accelerating web development cycles." (2020).
13. Nguyen, T., Chen, Z., & Han, S. (2021). GAN-based synthetic sensor data generation for autonomous vehicle training. IEEE Transactions on Intelligent Vehicles, 6(3), 477–487.
14. Zhang, X., Wu, H., & Wang, L. (2020). GAN-based anomaly detection for automotive CAN bus. IEEE Transactions on Vehicular Technology, 69(12), 15243–15254.
15. Sugumar, Rajendran (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification (14th edition). Int. J. Business Intelligence and Data Mining 14 (3):322-358.
16. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2020). Explainability and interpretability in machine learning models. Journal of Computer Science Applications and Information Technology, 5(1), 1–7. https://doi.org/10.15226/2474-9257/5/1/00148
17. Badmus, A., & Adebayo, M. (2020). Compliance-Aware Devops for Generative AI: Integrating Legal Risk Management, Data Controls, and Model Governance to Mitigate Deepfake and Data Privacy Risks in Synthetic Media Deployment.
18. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. ICML.
19. Liu, Y., Zhang, H., & Chen, Y. (2022). Cloud-based scalable anomaly detection for connected vehicles. IEEE Internet of Things Journal, 9(12), 9985–9995.