Please use this identifier to cite or link to this item:
Title: Antlion Optimization-Based Feature Selection Scheme for Cloud Intrusion Detection Using Naïve Bayes Algorithm
Authors: Christopher, Haruna Atabo
Abdulhamid, Shafi’i Muhammad
Misra, Sanjay
Odun-Ayo, I.
Sharma, M. M.
Keywords: Ant Lion Optimization
Cloud computing
Bayesian classifier
Feature selection
Issue Date: 3-Dec-2020
Publisher: International Conference on Intelligent Systems Design and Applications
Abstract: The popularity of cloud computing is due to its countless benefits which include flexibility, scalability, and cost effectiveness. This refers to the availability of services and computing resources on demand to users with little management drive via internet technology. One of the major challenges faced by this technol ogy is the issue of security which is making both service providers and users to worry about the safety of cloud resources. It is on this note that Cloud Intrusion Detection System (CIDS) is mostly deployed into cloud environment to identify and also prevent attacks in some instance. In this research work, a cloud intrusion detection system that identifies malicious activities inside cloud, utilizing Ant Lion Optimization (ALO) algorithm for feature selection and Bayesian Classifier was developed. Experimental result shows 96.22% accuracy, 0.379% FPR, 96.16% (Recall, Precision and F-Measure), and 92.36% Kappa Statistics.
Appears in Collections:Cyber Security Science

Files in This Item:
File Description SizeFormat 
55.pdf1.49 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.