Anomaly Detection for Insider Threats Using Unsupervised Ensembles
Article Properties
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DOI (url)
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Publication Date2021/06/01
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Indian UGC (journal)
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Refrences57
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Citations4
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Duc C. Le ORCID (unauthenticated)
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Nur Zincir-Heywood ORCID (unauthenticated)
Cite
Le, Duc C., and Nur Zincir-Heywood. “Anomaly Detection for Insider Threats Using Unsupervised Ensembles”. IEEE Transactions on Network and Service Management, vol. 18, no. 2, 2021, pp. 1152-64, https://doi.org/10.1109/tnsm.2021.3071928.
Le, D. C., & Zincir-Heywood, N. (2021). Anomaly Detection for Insider Threats Using Unsupervised Ensembles. IEEE Transactions on Network and Service Management, 18(2), 1152-1164. https://doi.org/10.1109/tnsm.2021.3071928
Le, Duc C., and Nur Zincir-Heywood. “Anomaly Detection for Insider Threats Using Unsupervised Ensembles”. IEEE Transactions on Network and Service Management 18, no. 2 (2021): 1152-64. https://doi.org/10.1109/tnsm.2021.3071928.
1.
Le DC, Zincir-Heywood N. Anomaly Detection for Insider Threats Using Unsupervised Ensembles. IEEE Transactions on Network and Service Management. 2021;18(2):1152-64.
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| PyoD: A Python toolbox for scalable outlier detection | 2019 | |||
| The wolf of SUTD (TWOS): A dataset of malicious insider threat behavior based on a gamified competition | 2018 | |||
| Supervised and unsupervised methods to detect insider threat from enterprise social and online activity data | 2015 | |||
| Isolation-based anomaly detection | 2012 | |||
| Scikit-learn: Machine learning in Python | 2011 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm | IEEE Access |
| 2024 | |
Network Intrusion Anomaly Detection Model Based on Multiclassifier Fusion Technology | Mobile Information Systems |
| 2023 | |
Memory-Augmented Insider Threat Detection with Temporal-Spatial Fusion | Security and Communication Networks |
| 1 | 2022 |
Detecting Insider Threat from Behavioral Logs Based on Ensemble and Self-Supervised Learning | Security and Communication Networks |
| 2 | 2021 |
Citations Analysis
The category
Science: Science (General): Cybernetics: Information theory 4
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Detecting Insider Threat from Behavioral Logs Based on Ensemble and Self-Supervised Learning and was published in 2021. The most recent citation comes from a 2024 study titled Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm. This article reached its peak citation in 2024, with 1 citations. It has been cited in 3 different journals, 33% of which are open access. Among related journals, the Security and Communication Networks cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.