Detection of insider threats using artificial intelligence and visualisation

Vasileios Koutsouvelis; Stavros Shiaeles; Bogdan Ghita; Gueltoum Bendiab

doi:10.1109/NetSoft48620.2020.9165337

Detection of insider threats using artificial intelligence and visualisation

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

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

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 language	English
Title of host publication	Proceedings of the 2020 IEEE Conference on Network Softwarization
Subtitle of host publication	Bridging the Gap Between AI and Network Softwarization, NetSoft 2020
Editors	Filip De Turck, Prosper Chemouil, Tim Wauters, Mohamed Faten Zhani, Walter Cerroni, Rafael Pasquini, Zuqing Zhu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	437-443
Number of pages	7
ISBN (Electronic)	978-1-7281-5684-2
ISBN (Print)	978-1-7281-5685-9
DOIs	https://doi.org/10.1109/NetSoft48620.2020.9165337
Publication status	Published - 12 Aug 2020
Event	6th IEEE Conference on Network Softwarization - Online, Belgium Duration: 29 Jun 2020 → 3 Jul 2020

Conference

Conference	6th IEEE Conference on Network Softwarization
Abbreviated title	NetSoft 2020
Country/Territory	Belgium
Period	29/06/20 → 3/07/20

Keywords

Artificial intelligence
Machine learning
Security
Threats
Visualization

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10.1109/NetSoft48620.2020.9165337

VASILEIOS_KOUTSOUVELIS_Insider_Threat_Paper-(fnl) v2.0
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Accepted author manuscript (Post-print), 733 KB

Cite this

Koutsouvelis, V., Shiaeles, S., Ghita, B., & Bendiab, G. (2020). Detection of insider threats using artificial intelligence and visualisation. In F. De Turck, P. Chemouil, T. Wauters, M. F. Zhani, W. Cerroni, R. Pasquini, & Z. Zhu (Eds.), Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020 (pp. 437-443). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NetSoft48620.2020.9165337

Koutsouvelis, Vasileios ; Shiaeles, Stavros ; Ghita, Bogdan et al. / Detection of insider threats using artificial intelligence and visualisation. Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020. editor / Filip De Turck ; Prosper Chemouil ; Tim Wauters ; Mohamed Faten Zhani ; Walter Cerroni ; Rafael Pasquini ; Zuqing Zhu. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 437-443

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title = "Detection of insider threats using artificial intelligence and visualisation",

abstract = "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.",

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booktitle = "Proceedings of the 2020 IEEE Conference on Network Softwarization",

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Koutsouvelis, V, Shiaeles, S, Ghita, B & Bendiab, G 2020, Detection of insider threats using artificial intelligence and visualisation. in F De Turck, P Chemouil, T Wauters, MF Zhani, W Cerroni, R Pasquini & Z Zhu (eds), Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020. Institute of Electrical and Electronics Engineers Inc., pp. 437-443, 6th IEEE Conference on Network Softwarization, Belgium, 29/06/20. https://doi.org/10.1109/NetSoft48620.2020.9165337

Detection of insider threats using artificial intelligence and visualisation. / Koutsouvelis, Vasileios; Shiaeles, Stavros; Ghita, Bogdan et al.
Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020. ed. / Filip De Turck; Prosper Chemouil; Tim Wauters; Mohamed Faten Zhani; Walter Cerroni; Rafael Pasquini; Zuqing Zhu. Institute of Electrical and Electronics Engineers Inc., 2020. p. 437-443.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Koutsouvelis V, Shiaeles S, Ghita B, Bendiab G. Detection of insider threats using artificial intelligence and visualisation. In De Turck F, Chemouil P, Wauters T, Zhani MF, Cerroni W, Pasquini R, Zhu Z, editors, Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 437-443 doi: 10.1109/NetSoft48620.2020.9165337

Detection of insider threats using artificial intelligence and visualisation

Abstract

Conference

Keywords

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