Intellectual Data Aggregation Using Independent Cluster based Medicaid Method for Network Functionality Fabrication

Objectives: Network functionality-based tracking is difficult for the fabrication processing under various environment condition. In order to achieve efficient result Intellectual data cluster-based Medicaid aggregation method applied network functionality analysis of attack on entities aggregate process balancing normal flow. Methods: The proposed Independent Cluster based Data Aggregation (ICAMA) compared with K-means and GSVM, DBSCAN algorithm classification understand response reinforcement against interconnection nodes by using energy weighted aggregation, accuracy metrics CIC-IDS 2019 dataset. Findings: Experiment studies provide 15.23% lower consumption comparing 17.18% algorithms. functional progress has aggregation15.07%,8.32%, 6.41%, 5.12%, 5.52%, 8.08% 2.21% given K-means, DBSCAN, SOM, PCA, ocSVM ICA methods also improves 11.07% GSVM method. ICAMA applies intellectual distribution in as a cluster or single process. Novelty: restore regular progress, continuously runs dynamic collection employing support identify types anomalies. Keywords: Clustering; aggregation; Energy consumption; Fabrication; attacks