An Effectual Stochastic Logitboost Based Discriminate Filter For Cloud Traceability And Analysis

Abstract

Abstract- Machine Learning is rapidly gaining popularity within the network monitoring community as the data produced by network devices and end-user terminals goes beyond the memory constraints of standard monitoring equipment. Critical network monitoring applications such as the detection of anomalies, network attacks and intrusions, require fast and continuous mechanisms for on-line analysis of data streams in cloud. In this paper we consider a stream-based machine learning approach for network security and anomaly detection, applying and evaluating multiple machine learning algorithms in the analysis of continuously evolving network data streams for security and storage. Here, Stochastic Logict model is designed for underlying data streams in cloud and Discriminate Filter based classifier for enhancing security. The proposed method is simulated in MATLAB environment and compared with existing techniques like Random forest approach and stochastic gradient boosting methods. This protocol design deals with both security and storage.