Detection of insider threats using artificial intelligence and visualisation

Vasileios Koutsouvelis*, Stavros Shiaeles, Bogdan Ghita, Gueltoum Bendiab

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Insider threats are one of the most damaging risk factors for the IT systems and infrastructure of a company or an organization; identification of insider threats has prompted the interest of the world academic research community, with several solutions having been proposed to alleviate their potential impact. For the implementation of the experimental stage described in this study, the Convolutional Neural Network (from now on CNN) algorithm was used and implemented via the Google Tensorflow program, which was trained to identify potential threats from images produced by the available dataset. From the examination of the images that were produced and with the help of Machine Learning, the question whether the activity of each user is classified as 'malicious' or not for the Information System was answered.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Conference on Network Softwarization
Subtitle of host publicationBridging the Gap Between AI and Network Softwarization, NetSoft 2020
EditorsFilip De Turck, Prosper Chemouil, Tim Wauters, Mohamed Faten Zhani, Walter Cerroni, Rafael Pasquini, Zuqing Zhu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)978-1-7281-5684-2
ISBN (Print)978-1-7281-5685-9
Publication statusPublished - 12 Aug 2020
Event6th IEEE Conference on Network Softwarization - Online, Belgium
Duration: 29 Jun 20203 Jul 2020


Conference6th IEEE Conference on Network Softwarization
Abbreviated titleNetSoft 2020


  • Artificial intelligence
  • Machine learning
  • Security
  • Threats
  • Visualization


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