Decision Support for Security-Control Identification Using Machine Learning

Seifeddine Bettaieb, Seung Yeob Shin, Mehrdad Sabetzadeh, Lionel Briand, Grégory Nou, Michael Garceau

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

[Context & Motivation] In many domains such as healthcare and banking, IT systems need to fulfill various requirements related to security. The elaboration of security requirements for a given system is in part guided by the controls envisaged by the applicable security standards and best practices. [Problem] An important difficulty that analysts have to contend with during security requirements elaboration is sifting through a large number of security controls and determining which ones have a bearing on the security requirements for a given system. This challenge is often exacerbated by the scarce security expertise available in most organizations. [Principal ideas/results] In this paper, we develop automated decision support for the identification of security controls that are relevant to a specific system in a particular context. Our approach, which is based on machine learning, leverages historical data from security assessments performed over past systems in order to recommend security controls for a new system. We operationalize and empirically evaluate our approach using real historical data from the banking domain. Our results show that, when one excludes security controls that are rare in the historical data, our approach has an average recall of (Formula Presented) 95% and average precision of (Formula Presented) 67%. [Contribution] The high recall – indicating only a few relevant security controls are missed – combined with the reasonable level of precision – indicating that the effort required to confirm recommendations is not excessive – suggests that our approach is a useful aid to analysts for more efficiently identifying the relevant security controls, and also for decreasing the likelihood that important controls would be overlooked.

Original languageEnglish
Title of host publicationRequirements Engineering
Subtitle of host publicationFoundation for Software Quality - 25th International Working Conference, REFSQ 2019, Proceedings
EditorsMichael Goedicke, Eric Knauss
PublisherSpringer Verlag
Pages3-20
Number of pages18
ISBN (Print)9783030155377
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event25th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2019 - Essen, Germany
Duration: 18 Mar 201921 Mar 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11412 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2019
Country/TerritoryGermany
CityEssen
Period18/03/1921/03/19

Keywords

  • Machine learning
  • Security assessment
  • Security requirements engineering

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