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X-ware: a proof of concept malware utilizing artificial intelligence

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 2 (2022)

DOI
10.11591/ijece.v12i2.pp1937-1944
ISSN
2722-2578, 2088-8708
Published
2022-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research literature techniques. This article investigates the possibility of combining artificial neural networks and swarm intelligence to generate a new type of malware. We successfully created a proof of concept malware named X-ware, which we tested against the Windows-based systems. Developing this proof of concept may allow us to identify this potential threat’s characteristics for developing mitigation methods in the future. Furthermore, a method for recording the virus’s behavior and propagation throughout a file system is presented. The proposed virus prototype acts as a swarm system with a neural network-integrated for operations. The virus’s behavioral data is recorded and shown under a complex network format to describe the behavior and communication of the swarm. This paper has demonstrated that malware strengthened with computational intelligence is a credible threat. We envisage that our study can be utilized to assist current and future security researchers to help in implementing more effective countermeasures

Collaboration network of the authors

4 authors · 6 co-authorship links · 3 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • VSB - Technical University of Ostrava
  • University of Finance - Marketing
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>X-ware: a proof of concept malware utilizing artificial intelligence</dc:title><dc:creator>Truong, Thanh Cong</dc:creator><dc:creator>Plucar, Jan</dc:creator><dc:creator>Diep, Bao Quoc</dc:creator><dc:creator>Zelinka, Ivan</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;p&amp;gt;Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research literature techniques. This article investigates the possibility of combining artificial neural networks and swarm intelligence to generate a new type of malware. We successfully created a proof of concept malware named X-ware, which we tested against the Windows-based systems. Developing this proof of concept may allow us to identify this potential threat’s characteristics for developing mitigation methods in the future. Furthermore, a method for recording the virus’s behavior and propagation throughout a file system is presented. The proposed virus prototype acts as a swarm system with a neural network-integrated for operations. The virus’s behavioral data is recorded and shown under a complex network format to describe the behavior and communication of the swarm. This paper has demonstrated that malware strengthened with computational intelligence is a credible threat. We envisage that our study can be utilized to assist current and future security researchers to help in implementing more effective countermeasures&amp;lt;/p&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i2.pp1937-1944</dc:identifier><dc:identifier>10.11591/ijece.v12i2.pp1937-1944</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 2 (2022); 1937</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/25216/15582</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>