1 |
R. Nasir, M. Afzal, R. Latif, and W. Iqabl, "Behavioral Based Insider Threat Detection Using Deep Learning," IEEE Access, vol. 9, pp. 143266-143274, Oct. 2021.
DOI
|
2 |
A. Khan, R. Latif, S. Latif, S. Tahir, G. Batool, and T. Saba, "Malicious Insider Attack Detection in IoTs Using Data Analytics," IEEE Access, vol. 8, pp. 11743-11753, Dec. 2019.
DOI
|
3 |
D.C. Le, N. Zincir-Heywood, and M.I. Heywood, "Analyzing Data Granularity Levels for Insider Threat Detection Using Machine Learning," IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 30-44, Mar. 2020.
DOI
|
4 |
H. Bian, T. Bai, M.A. Salahuddin, N. Limam, A.A. Daya, and R. Boutaba, "Uncovering Lateral Movement Using Authentication Logs," IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 1049-1063, Mar. 2021.
DOI
|
5 |
Software Engineering Institute, Carnegie Mellon University, "CERT Insider Threat Test Dataset" https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=508099, Accessed: 20 Sept. 2021.
|
6 |
G. Kaiafas, G. Varisteas, S. Lagraa, R. State, C.D. Nguyen, T. Ries, and M. Ourdane, "Detecting Malicious Authentication Events Trustfully," Proceedings of the 2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1-6, Apr. 2018.
|
7 |
T. Yang, Y. Lin, C. Wu, and C. Wang, "Voting-Based Ensemble Model for Network Anomaly Detection," Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8543-8547, Jun. 2021.
|
8 |
B.A. Tama, M. Comuzzi, and K. Rhee, "TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System," IEEE Access, vol. 7, pp. 94497-94507, Jul. 2019.
DOI
|
9 |
P. Domingos, " Unified Bias-Variance Decomposition and its Applications," Proceedings of the 17th International Conference on Machine Learning, pp. 231-238, Jun. 2000.
|
10 |
Scott Fortmann-Roe, "Understanding the Bias-Variance Tradeoff" http://scott.fortmann-roe.com/docs/BiasVariance.html, Accessed: 31 Nov. 2021.
|
11 |
scikit-learn, "Machine Learning in Python" https://scikit-learn.org/stable, Accessed: 31 Oct. 2021.
|
12 |
G.E. Batista, A.L.C. Bazzan, and M. Monard, "Balancing Training Data for Automated Annotation of Keywords: a Case Study," Proceedings of the 2nd Brazilian Workshop on Bioinformatics, pp. 35-43, Jan. 2003.
|
13 |
D.C. Le and A.N. Zincir-Heywood, "Machine learning based Insider Threat Modelling and Detection," Proceedings of the 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, pp. 1-6, Apr. 2019.
|
14 |
N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, no.1, pp. 321-357, Jun. 2002.
DOI
|
15 |
H. He, Y. Bai, E.A. Garcia, and S. Li, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning," Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1322-1328, Jul. 2008.
|
16 |
Gurucul, "2021 Insider Threat Report" https://gurucul.com/2021-insider-threat-report, Accessed: 25 Oct. 2021.
|
17 |
D.C. Le and N. Zincir-Heywood, "Anomaly Detection for Insider Threats Using Unsupervised Ensembles," IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 1152-1164, Jun. 2021.
DOI
|
18 |
Scikit-learn, "6.3. Preprocessing data" https://scikit-learn.org/stable/modules/preprocessing.html, Accessed: 30 Oct. 2021.
|
19 |
J. Jiang, J. Chen, T. Gu, K.R. Choo, C. Liu, M. Yu, W. Huang, and P. Mohapatra, "Anomaly Detection with Graph Convolutional Networks for Insider Threat and Fraud Detection," Proceedings of the 2019 IEEE Military Communications Conference, pp. 109-114, Nov. 2019.
|