@inproceedings{Le_spw2018,
 abstract = {Insider threat is a prominent cyber-security dan- ger faced by organizations and companies. In this research, we study and evaluate an insider threat detection workflow using supervised and unsupervised learning algorithms. To this end, we study data exploration and analysis, anomaly detection and malicious behaviour classification on a publicly available data set. We evaluate several supervised and unsupervised learning algorithms - HMM, SOM, and DT - using this workflow.},
 author = {Le, Duc C. and Zincir-Heywood, A. Nur},
 title = {Evaluating Insider Threat Detection Workflow Using Supervised and Unsupervised Learning},
 booktitle = {IEEE Security and Privacy Workshops (SPW '18)},
 year = {2018},
 address = {San Francisco, CA, USA},
 pages = {270-275},
 doi = {10.1109/SPW.2018.00043},
 keywords = {Insider Threat Detection, Unsupervised Machine Learning, Supervised Machine Learning, Anomaly detection, Behaviour Classification},
 url = {https://ieeexplore.ieee.org/abstract/document/8424659/},
 url_Paper = {lcd_spw2018.pdf}
}

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