hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Hybrid Machine Learning Approach for Malicious Behaviour Detection and Recognition in Cloud Computing
Nanjing University of Science and Technology, Nanjing, China.
Nanjing University of Science and Technology, Nanjing, China.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Nanjing University of Science and Technology, Nanjing, China.
2020 (English)In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 151, article id 102507Article in journal (Refereed) Published
Abstract [en]

The rapid growth of new emerging computing technologies has encouraged many organizations to outsource their data and computational requirements. Such services are expected to always provide security principles such as confidentiality, availability and integrity; therefore, a highly secure platform is one of the most important aspects of cloud-based computing environments. A considerable improvement over traditional security strategies is achieved by understanding how malware behaves over the entire behavioural space. In this paper, we propose a new approach to improve the capability of cloud service providers to model users’ behaviours. We applied a particle swarm optimization-based probabilistic neural network (PSO-PNN) for the detection and recognition process, in the first module of the recognition process, we meaningfully converted the users’ behaviours to an understandable format and then classified and recognized the malicious behaviours by using a multi-layer neural network. We took advantage of the UNSW-NB15 dataset to validate the proposed solution by characterizing different types of malicious behaviours exhibited by users. Evaluation of the experimental results shows that the proposed method is promising for use in security monitoring and recognition of malicious behaviours. © 2019 Elsevier Ltd

Place, publisher, year, edition, pages
London: Academia Press, 2020. Vol. 151, article id 102507
Keywords [en]
Malicious behaviour recognition, Intrusion detection, Particle Swarm Optimization, Probabilistic Neural Network
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41270DOI: 10.1016/j.jnca.2019.102507Scopus ID: 2-s2.0-85076846833OAI: oai:DiVA.org:hh-41270DiVA, id: diva2:1379407
Note

Funding: National Natural Science Foundation of China (61170035, 61272420, 81674099, 61502233), the Fundamental Research Fund for the Central Universities (30916011328, 30918015103), the Nanjing Science and Technology Development Plan Project (201805036), and the “13th Five-Year” Equipment Field Fund (61403120501).

Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-02-17

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Khoshkangini, Reza

Search in DiVA

By author/editor
Khoshkangini, Reza
By organisation
CAISR - Center for Applied Intelligent Systems Research
In the same journal
Journal of Network and Computer Applications
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 91 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf