Attack vectors on supervised machine learning systems in business applications Cover Image

Wektory ataków na nadzorowane systemy uczące się w zastosowaniach biznesowych
Attack vectors on supervised machine learning systems in business applications

Author(s): Jerzy Surma
Subject(s): ICT Information and Communications Technologies
Published by: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Keywords: adversarial machine learning; supervised machine learning; security of machine learning systems

Summary/Abstract: Machine learning systems have become incredibly popular and now have practical applications in many fields. An area of business applications has been developing particularly well, starting from the prediction of customers’ purchase preferences and up to the automation of critical business processes. In this context, the security of such systems in a situation of a threat of intentional attacks carried by organized crime is extremely important. A theoretical framework of attacks on supervised machine learning systems, which are the most popular in business applications, is set out in this article. The possible attack vectors are widely discussed. The main contribution of this article is to recognize that the black box type attack scenario is the most probable, therefore the scenario of this kind of attacks was described extensively.

  • Issue Year: 57/2020
  • Issue No: 3
  • Page Range: 65-72
  • Page Count: 8
  • Language: English
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