Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin
Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be “transparent” to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out.
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Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin
Semantic Scholar · Computer Science · 2021
Abstract
Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be “transparent” to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out.