Insider threat detection using deep autoencoder and variational autoencoder neural networks

Efthimios Pantelidis, Gueltoum Bendiab, Stavros Shiaeles, Nicholas Kolokotronis

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

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Abstract

Internal attacks are one of the biggest cybersecurity issues to companies and businesses. Despite the implemented perimeter security systems, the risk of adversely affecting the security and privacy of the organization’s information remains very high. Actually, the detection of such a threat is known to be a very complicated problem, presenting many challenges to the research community. In this paper, we investigate the effectiveness and usefulness of using Autoencoder and Variational Autoencoder deep learning algorithms to automatically defend against insider threats, without human intervention. The performance evaluation of the proposed models is done on the public CERT dataset (CERT r4.2) that contains both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a higher detection accuracy and a reasonable false positive rate.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Cyber Security and Resilience (CSR)
PublisherIEEE
Pages129-134
ISBN (Electronic)9781665402859
ISBN (Print)9781665402866
DOIs
Publication statusPublished - 6 Sep 2021
Event2021 IEEE International Conference on Cyber Security and Resilience - Virtual
Duration: 26 Jul 202128 Jul 2021
https://www.ieee-csr.org/

Conference

Conference2021 IEEE International Conference on Cyber Security and Resilience
Abbreviated titleIEEE CSR
Period26/07/2128/07/21
Internet address

Keywords

  • Deep Learning
  • Insider Threat
  • Network Security
  • Anomaly Detection

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