Detail výsledku
Final Report - Machine Learning Outlier Detectionin Safetica's Data Loss Prevention System
Data loss prevention systems are becoming necessities incorporate computer system deployments. Nowadays, when everything is connected, and BYOD (Bring your own device) methodology is tolerated, even encouraged in many companies, network security administrators are obliged to keep with newest technologies to prevent threats to business resources. Threats might be parts of carefully planned corporate espionage, or simple malware encrypting all resources available to it. No matter which threat, data have to be kept safe and each interaction with critical business resources need to be monitored, authorized and logged for future analysis. In this paper, we discuss state of the art methods used for outlier detection, unsupervised learning, and statistical analysis.
The final report describes designed technical solution, methods that were implemented and their performance.
Machine learning, Outlier detection, Data loss prevention
@misc{BUT146363,
author="Jan {Pluskal}",
title="Final Report - Machine Learning Outlier Detectionin Safetica's Data Loss Prevention System",
year="2017",
pages="16",
publisher="Safetica Services s.r.o",
address="Praha",
url="https://www.fit.vut.cz/research/publication/11599/",
note="Summary research report"
}