| Title | Insider Threat Detection using Deep Autoencoder and Variational Autoencoder Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Pantelidis, Efthimios, Bendiab, Gueltoum, Shiaeles, Stavros, Kolokotronis, Nicholas |
| Conference Name | 2021 IEEE International Conference on Cyber Security and Resilience (CSR) |
| Keywords | anomaly detection, composability, Costs, Data models, Deep Learning, Human Behavior, insider threat, Metrics, Network security, Neural networks, performance evaluation, Planning, policy-based governance, privacy, pubcrawl |
| 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. |
| DOI | 10.1109/CSR51186.2021.9527925 |
| Citation Key | pantelidis_insider_2021 |
Insider Threat Detection using Deep Autoencoder and Variational Autoencoder Neural Networks