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Antlion Optimization-Based Feature Selection Scheme for Cloud Intrusion Detection Using Naïve Bayes Algorithm

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

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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 technology 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.

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Acknowledgement

The authors appreciate the Covenant University through its Centre for Research, Innovation and Discovery for Financial assistance and sponsorship.

Author information

Authors and Affiliations

  1. Federal University of Technology, Minna, Nigeria

    Haruna Atabo Christopher & Shafi’i Muhammad Abdulhamid

  2. Covenant University, Ota, Nigeria

    Sanjay Misra & Isaac Odun-Ayo

  3. Zillow Inc., San Francisco, USA

    Mayank Mohan Sharma

Authors
  1. Haruna Atabo Christopher
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  2. Shafi’i Muhammad Abdulhamid
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  3. Sanjay Misra
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  4. Isaac Odun-Ayo
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  5. Mayank Mohan Sharma
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Corresponding author

Correspondence to Sanjay Misra .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Christopher, H.A., Abdulhamid, S.M., Misra, S., Odun-Ayo, I., Sharma, M.M. (2021). Antlion Optimization-Based Feature Selection Scheme for Cloud Intrusion Detection Using Naïve Bayes Algorithm. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_128

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