A Forecasting Approach to Improve Control and Management for 5G Networks
Product Description
Objective
To design an efficient algorithm for detection of anomaly in 5G network and perform efficient forecast model.
Abstract
Accurate and autonomous traffic forecasting is essential for many traffic management decisions in a network, such as traffic accounting, short-time traffic scheduling, long-term capacity planning, network design or network anomaly detection, and even multi-slice management in 5G networks. In the existing system, Dynamic threshold algorithm is used to detect the abnormality in 5G networks and formulates the forecast approach to manage the network effectively. The existing system focused on managing the present network to utilize the available resource and mange the layers with integrated 5G architecture. In the proposed system, LYSIS dataset is considered in which the IoT traffic data activated with 5G networks are recorded. The system manage the network effectively through Categorical Boosting algorithm (Cat-Boost) as well as find the anomaly present in the network. The malwares such as phishing probes are detected from the presented system. The performance of the system is measured using RMSE with respect to the obtained results.
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Mini Projects - Hardware includes
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support
The Delivery time for Hardware Mini projects is 7-8 working days.