Industrial Internet of Things (IIoT); cybersecurity; intrusion detection; dataset
Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data.
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Scalability, heterogeneity, energy efficiency, cost-effectiveness, robustness, interoperability, and low latency data transfer are some of the critical challenges posed by the Internet of Things in the modern era of the Internet. Content Centric Networks (CCN) and Named Data Networks (NDN) are some proposed solutions that can meet the abovementioned challenges. In-network caching, multicasting, content security, and decoupling of data from location are the significant advantages offered by the CCN.
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Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.
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