Malicious insider threat detection using variation of sampling methods for anomaly detection in cloud environment
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Al-Shehari, T., & Alsowail, R. A. (2021) · Entropy · doi:10.3390/e23101258 · Cited by 193 · see citing works
Insider Threat Detection with Deep Neural Network
Yuan, F., Cao, Y., Shang, Y., Liu, Y., Tan, J., & Fang, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93698-7_4 · Cited by 168 · see citing works
Scenario-Based Insider Threat Detection From Cyber Activities
Chattopadhyay, P., Wang, L., & Tan, Y.-P. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2857473 · Cited by 115 · see citing works
Anomaly-Based Insider Threat Detection Using Deep Autoencoders
Liu, L., De Vel, O., Chen, C., Zhang, J., & Xiang, Y. (2018) · 2018 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw.2018.00014 · Cited by 79 · see citing works
Handling Insider Threat Through Supervised Machine Learning Techniques
Janjua, F., Masood, A., Abbas, H., & Rashid, I. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.10.012 · Cited by 48 · see citing works
Exploring Adversarial Properties of Insider Threat Detection
Le, D. C., & Zincir-Heywood, N. (2020) · 2020 IEEE Conference on Communications and Network Security (CNS) · doi:10.1109/cns48642.2020.9162254 · Cited by 17 · see citing works
Classifier Suites for Insider Threat Detection
Noever, D. (2019) · arXiv · doi:10.48550/arxiv.1901.10948 · Cited by 16 · see citing works
Padmavathi, G., Shanmugapriya, D., & Asha, S. (2022) · 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) · doi:10.23919/indiacom54597.2022.9763205 · Cited by 15 · see citing works
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Alzaabi, F. R., & Mehmood, A. (2024) · IEEE Access · doi:10.1109/access.2024.3369906 · Cited by 204 · see citing works
“For instance, advanced algorithms could incorporate contextual information such as time-of-day, location, and user roles to improve the accuracy of threat detection [3].”
BackgroundInfluentialISFL-AE: Insider-Specific Feature Learning Autoencoder for Lightweight Insider Threat Detection
Kim, Y., & Kim, Y.-G. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3659000 · Cited by 1 · see citing works
- Authors
- S, A., D, S., & G, P.
- ORCID
- D, Shanmugapriya
- Venue
- Computers and Electrical Engineering
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 105
- Pages
- 108519
- ISSN
- 0045-7906 (ISSN-L)
- OpenAlex
- W4311988569
- Cited by
- 27
- References
- 8
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 14 · 2023: 2
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Electrical Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- class imbalance, sampling methods, oversampling, undersampling, anomaly detection, cloud security, support vector machine, one-class support vector machine
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