Publication Details

Machine Learning Outlier Detection in Safetica's Data Loss Prevention System

PLUSKAL, J. Machine Learning Outlier Detection in Safetica's Data Loss Prevention System. Praha: Safetica Services s.r.o, 2017. p. 0-0.
Czech title
Detekce anomálií za účelem prevence ztráty dat
Type
summary research report - contract. research
Language
English
Authors
Keywords

Machine learning, Outlier detection, Data loss prevention

Abstract

Data loss prevention systems are becoming necessities in corporate 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.

Published
2017
Pages
16
Publisher
Safetica Services s.r.o
Place
Praha
BibTeX
@misc{BUT146362,
  author="Jan {Pluskal}",
  title="Machine Learning Outlier Detection in 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/11598/",
  note="summary research report - contract. research"
}
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