Secure Data In Cloud Using Machine Learning Based On Intrusion Detection System

Huge amounts of network traffic are produced daily due to the introduction new technologies like cloud computing and big data, intrusion detection system must dynamically gather evaluate data by incoming traffic. However, not all features in a huge dataset help describe traffic, so limiting choosing only few suitable may increase system's speed accuracy. Based on rate each feature has established throughout selection process, method been developed this study remove irrelevant identify that would enhance rate. Recursive reduction was used association with decision tree-based classifier accomplish goal, appropriate relevant were later found. This methodology utilized research, along scikit-learn, machine learning toolkit built Python, analyze NSL-KDD dataset, an upgraded version KDD 1999 dataset. allowed for identification within improving accuracy rates. Machine uses C4.5 classification tree method, which is based ID3 algorithm. The assumption considerably enhances performance supported these findings.