Towards Improved Network Security Requirements and Policy: Domain-Specific Completeness Analysis via Topic Modeling

Jane Huffman Hayes, Jared Payne, Emily Essex, Kelsey Cole, Joseph Alverson, Alex Dekhtyar, Dongfeng Fang, Grant Bernosky

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

Abstract

Network security policies contain requirements-including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.

Original languageEnglish
Title of host publicationProceedings - 7th International Workshop on Artificial Intelligence and Requirements Engineering, AIRE 2020
Pages83-86
Number of pages4
ISBN (Electronic)9781728183527
DOIs
StatePublished - Sep 2020
Event7th International Workshop on Artificial Intelligence and Requirements Engineering, AIRE 2020 - Zurich, Switzerland
Duration: Sep 1 2020 → …

Publication series

NameProceedings - 7th International Workshop on Artificial Intelligence and Requirements Engineering, AIRE 2020

Conference

Conference7th International Workshop on Artificial Intelligence and Requirements Engineering, AIRE 2020
Country/TerritorySwitzerland
CityZurich
Period9/1/20 → …

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Funding

ACKNOWLEDGMENT We thank NSF for partially funding this work under grant CICI 1642134. We thank the class of CalPoly students for undertaking the study. We thank Dr. Tingting Yu for helpful discussions on experimental design.

FundersFunder number
National Science Foundation (NSF)1642134, CICI 1642134

    Keywords

    • Requirements quality
    • completeness
    • empirical evaluation
    • machine learning
    • network security

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Safety, Risk, Reliability and Quality

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