Gauss Markov and Flow Balanced Vector Radial Learning network traffic classification on IoT with SDN.

ABSTRACT: Recent evolution in connected devices modelled a massive stipulation for network traffic resources and classification. Software-defined networking (SDN) enables ML techniques with the Internet of Things (IoT) to automate network traffic. This helps to reduce accuracy and improves latency. Problems by conventional techniques to categorize network traffic acquired from IoT and assign resources can be resolved through SDN solutions. This manuscript proposes a proposed network traffic classification technique on IoT with SDN called Gauss Markov and Flow-balanced Vector Radial Learning (GM-FVRL). With the network traffic features acquired from the IoT devices, SDN-enabled Gauss Markov Correlation-based IoT Network Traffic Feature Extraction is applied to extort relevant network aspects. Next, t

SUBMITTER: Kulandaivel R 

PROVIDER: S-EPMC11444398 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature