Metaheuristics with Machine Learning Enabled Information Security on燙loud Environment

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud environment. Cloud computing (CC) is used for storing as well as processing data. Therefore, security becomes important as the CC handles massive quantity of outsourced, and unprotected sensitive data for public access. This study introduces a novel chaotic chimp optimization with machine learning enabled information security (CCOML-IS) technique on cloud environment. The proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the network. The proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max normalization. Followed by, the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm (CCOA). In addition, kernel ridge regression (KRR) classifier is used for the detection of security issues in the network. The design of CCOA technique assists in choosing optimal features and thereby boost the classification performance. A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several measures. The comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures.