A Comparative Analysis of Threat Modelling Methods: STRIDE, DREAD, VAST, PASTA, OCTAVE, and LINDDUN

Nitin Naik, Paul Jenkins, Paul Grace, Dishita Naik, Shaligram Prajapat, Jingping Song

Research output: Chapter in Book/Published conference outputConference publication

7 Citations (SciVal)

Abstract

Novel cybersecurity threats are constantly emerging and posing significant security challenges to organisations; therefore, it is important for organisations to proactively analyse existing and emerging cybersecurity threats against their systems. Threat modelling methods are very effective in proactively analysing cybersecurity threats and enhancing organisational security policies and defence mechanisms against these cybersecurity threats. Several threat modelling methods have been proposed, and it is important for security experts to select the appropriate threat modelling method for an organisation according to their specific security challenges and cybersecurity threats. This paper will present a comparative analysis of six threat modelling methods: STRIDE, DREAD, VAST, PASTA, OCTAVE, and LINDDUN. It will provide a concise description of all the aforementioned threat modelling methods, and subsequently, a comparative analysis of these six threat modelling methods for highlighting their relative strengths and limitations.
Original languageEnglish
Title of host publicationContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK: The C3AI 2024
EditorsNitin Naik, Paul Jenkins, Shaligram Prajapat, Paul Grace
Pages271-280
ISBN (Electronic)978-3-031-74443-3
DOIs
Publication statusPublished - 20 Dec 2024

Publication series

NameLecture Notes in Networks and Systems (LNNS)
PublisherSpringer Cham
Volume884
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

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