An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier

Haruna Atabo Christopher*, Jimoh Yakubu**, Shafi’i Muhammad Abdulhamid***, Abdulmalik D. Mohammed****
*-** PG Scholar, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.
*** Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.
**** Research Scholar, University of Manchester, United Kingdom.
Periodicity:July - December'2018


Cloud computing has become popular due to its numerous advantages, which include high scalability, flexibility, and low operational cost. It is a technology that gives access to shared pool of resources and services on pay per use and at minimum management effort over the internet. Because of its distributed nature, security has become a great concern to both cloud service provider and cloud users. That is why Cloud Intrusion Detection System (CIDS) has been widely used to the cloud computing setting, which detects and in some cases prevents intrusion. In this paper, the authors have proposed a conceptual framework that detects intrusion attacks within the cloud environment using Ant Lion Optimization (ALO) algorithm for feature selection and Bayesian Classifier. This framework is expected to detect cloud intrusion accurately at low computational cost and reduce false alert rate.


Ant Lion optimization; Bayesian Classifier; CIDS; feature selection; cloud computing.

How to Cite this Article?

Christopher,H.A., Yakubu.J., Abdulhamid,S.M., Mohammed,A.D.(2018). An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier, i-manager's Journal on Cloud Computing 5(2), 36-44.


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