Metaheuristics with Machine Learning Enabled Information Security on Cloud Environment

作     者：Haya Mesfer Alshahrani Faisal S.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen 

作者机构：Department of Information SystemsCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of CybersecurityCollege of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems-Girls SectionKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer ScienceCollege of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：1557-1570页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/49/42) Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia. 

主　　题：Information security cloud computing intrusion anomalies data mining feature selection classification 

摘      要：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.