Synthetic Network Traffic Generation in IoT Supply Chain Environment

Skrodelis, Heinrihs Kristians; Romānovs, Andrejs

doi:10.1109/itms56974.2022.9937126

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…The IoMT dataset was generated using IoT-Flock, an open-source tool that can generate IoT synthetic traffic with many scenarios depending on a user's choice [29]. The scenario created by the authors of the ICUDatasetProcessed dataset is based on an Intensive Care Unit (ICU).…”

Section: Datasetmentioning

confidence: 99%

Resilient Security Framework Using TNN and Blockchain for IoMT

et al. 2023

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The growth of the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to various cybersecurity attacks and threats, which lead to negative consequences. Cyberattacks can damage not just the IoMT devices in use but also human life. Currently, several security solutions have been proposed to enhance the security of the IoMT, employing machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. Furthermore, blockchain technology enables a decentralized approach to the healthcare system, eliminating some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects malicious data measured by medical sensors to find fraudulent data. As a result, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. Additionally, a blockchain network is used in the fog layer to ensure that the data is not altered, enhancing the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% based on the F1-score accuracy metric.

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Section: Datasetmentioning

confidence: 99%