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<p>This paper presents SereWay, an open-source testbed for benchmarking the detection of security and reliability-related anomalies in the context of the railway Industrial Internet of Things domain. The proposal leverages lightweight virtualization and synthetic data generation modules trained from real train sensor data to create both normal and faulty behavior of the system, while Distributed Denial of Service attacks are generated through a BotNet simulation framework. The envisioned solution aims to facilitate the benchmarking of Machine Learning-based security and reliability solutions for the railway domain, and it is built on top of Open-<span>fari</span>, an existing railway testbed for anomaly detection. The proposal has been used in the context of an experimental campaign for the evaluation of a Federated Learning-based system aiming at detecting both security and reliability anomalies. The obtained results highlight the potentialities of the proposed framework to emulate realistic railway scenarios and support the benchmarking of Machine Learning-based security and reliability instruments in this context.</p>

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SereWay: Toward SEcurity and REliability Benchmarking for the RailWAY IIoT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

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  • Alessandra Rizzardi,
  • Raffaele Della Corte,
  • Jesús F. Cevallos M.,
  • Simona De Vivo,
  • Sabrina Sicari,
  • Domenico Cotroneo,
  • Alberto Coen-Porisini

摘要

This paper presents SereWay, an open-source testbed for benchmarking the detection of security and reliability-related anomalies in the context of the railway Industrial Internet of Things domain. The proposal leverages lightweight virtualization and synthetic data generation modules trained from real train sensor data to create both normal and faulty behavior of the system, while Distributed Denial of Service attacks are generated through a BotNet simulation framework. The envisioned solution aims to facilitate the benchmarking of Machine Learning-based security and reliability solutions for the railway domain, and it is built on top of Open-fari, an existing railway testbed for anomaly detection. The proposal has been used in the context of an experimental campaign for the evaluation of a Federated Learning-based system aiming at detecting both security and reliability anomalies. The obtained results highlight the potentialities of the proposed framework to emulate realistic railway scenarios and support the benchmarking of Machine Learning-based security and reliability instruments in this context.