Collaborative anomaly detection framework for handling big data of cloud computing

Nour Moustafa, Gideon Creech, Elena Sitnikova, Marwa Keshk

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

47 Citations (Scopus)

Abstract

With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based services to its customers. Nevertheless, security and privacy are the main challenges to its success due to its dynamic and distributed architecture, resulting in generating big data that should be carefully analysed for detecting network's vulnerabilities. In this paper, we propose a Collaborative Anomaly Detection Framework (CADF) for detecting cyber attacks from cloud computing environments. We provide the technical functions and deployment of the framework to illustrate its methodology of implementation and installation. The framework is evaluated on the UNSW-NB15 dataset to check its credibility while deploying it in cloud computing environments. The experimental results showed that this framework can easily handle large-scale systems as its implementation requires only estimating statistical measures from network observations. Moreover, the evaluation performance of the framework outperforms three state-of-the-art techniques in terms of false positive rate and detection rate.

Original languageEnglish
Title of host publication2017 Military Communications and Information Systems Conference, (MilCIS)
Subtitle of host publicationMilCIS 2017 - Proceedings
Place of PublicationMassachusetts, U.S.A.
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509040032
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event2017 Military Communications and Information Systems Conference, MilCIS 2017 - Canberra, Australia
Duration: 14 Nov 201716 Nov 2017

Conference

Conference2017 Military Communications and Information Systems Conference, MilCIS 2017
Country/TerritoryAustralia
CityCanberra
Period14/11/1716/11/17

Keywords

  • Cloud Computing
  • Collaborative Anomaly Detection Framework
  • Gaussian Mixture Model (GMM)
  • Interquartile Range (IQR)
  • UNSW-NB15 dataset

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