A New Approach for Machine Learning Security Risk Assessment - Work in Progress

Jun Yajima, Maki Inui, Takanori Oikawa, Fumiyoshi Kasahara, Ikuya Morikawa, Nobukazu Yoshioka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a new security risk assessment approach for Machine Learning-based AI systems (ML systems). The assessment of security risks of ML systems requires expertise in ML security. So, ML system developers, who may not know much about ML security, cannot assess the security risks of their systems. By using our approach, a ML system developers can easily assess the security risks of the ML system. In performing the assessment, the ML system developer only has to answer the yes/no questions about the specification of the ML system. In our trial, we confirmed that our approach works correctly.

Original languageEnglish
Title of host publicationProceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-53
Number of pages2
ISBN (Electronic)9781450392754
DOIs
Publication statusPublished - 2022
Event1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022 - Pittsburgh, United States
Duration: 2022 May 162022 May 17

Publication series

NameProceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022

Conference

Conference1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
Country/TerritoryUnited States
CityPittsburgh
Period22/5/1622/5/17

Keywords

  • ML Security
  • Machine Learning
  • Risk Assessment

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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