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 language | English |
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Title of host publication | 2021 IEEE International Conference on Cyber Security and Resilience (CSR) |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 129-134 |
ISBN (Electronic) | 9781665402859 |
ISBN (Print) | 9781665402866 |
DOIs | |
Publication status | Published - 6 Sept 2021 |
Event | 2021 IEEE International Conference on Cyber Security and Resilience - Virtual Duration: 26 Jul 2021 → 28 Jul 2021 https://www.ieee-csr.org/ |
Conference
Conference | 2021 IEEE International Conference on Cyber Security and Resilience |
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Abbreviated title | IEEE CSR |
Period | 26/07/21 → 28/07/21 |
Internet address |
Keywords
- Deep Learning
- Insider Threat
- Network Security
- Anomaly Detection