Detail publikace

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

PLUSKAL, J.

Originální název

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

Typ

souhrnná výzkumná zpráva - smluv. výzkum

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Machine learning, Outlier detection, Data loss prevention

Autoři

PLUSKAL, J.

Vydáno

28. 7. 2017

Nakladatel

Safetica Services s.r.o

Místo

Praha

Strany počet

16

URL

https://www.fit.vut.cz/research/publication/11598/

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"
}