• Login
    View Item 
    •   MUT Research Archive
    • Journal Articles
    • School of Computing and IT (JA)
    • Journal Articles (CI)
    • View Item
    •   MUT Research Archive
    • Journal Articles
    • School of Computing and IT (JA)
    • Journal Articles (CI)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Social Attackability Metrics for Software Systems

    Thumbnail
    View/Open
    Social Attackability Metrics for Software Systems.pdf (487.7Kb)
    Date
    2013
    Author
    Mbuguah, S.M.
    Mwangi, W.
    Muketha, Geoffrey M.
    Metadata
    Show full item record
    Abstract
    Software based system have become ubiquitous in modern day activities. Software system based system are being increasing attacked, leading to the need for software system administrators, and managers to have some metrics at predicting the social engineering attackability of a such system. Researchers have identified seven human traits/attributes that make human susceptible to social engineering attacks. Yet they did not model nor come up metrics. The author has published a conceptual a holistic predictive attackability metric model and corresponding metrics to assist the system designers. The model considers the technical metrics based on cohesion, coupling and complexity as used to predict attackability. It also consider the social metrics based on human traits that make the human operators become susceptible to social engineering attacks. The identified human traits are dishonesty, social compliance, Kindness,Time pressure, Herd mentality, greed/need and distraction. This paper considers only the social metrics part of the model.To measure human traits the authors relies on the HEXACO model and Big Five personality trait models. In these model the personality trait are measured using a ranking scale based on Lickert scale. Hence each trait is measured as a percentile. However, for purpose of this paper, to postulate the metric the author considered the discrete case. Why the value of trait take either a value of “1” or “0”. To determine the relationship between traits between and attackability experts were asked to assess the trait versus attackability from which after aggregating for all traits a social attackability metrics was determined. To determine the predictive social attackability metrics each trait was considered to be equally likely to occur and hence a probability of 1/7 and this acts as factor to transform the social attackability metric into predictive attackability metrics.
    URI
    http://hdl.handle.net/123456789/275
    https://www.techrepublic.com/resource-library/whitepapers/social-attackability-metrics-for-software-systems/
    https://www.semanticscholar.org/paper/Social-Attackability-Metrics-for-Software-Systems-Mbuguah-Mwangi/3c75e217ab4afc6ec5861dc1dccc1bb4c515d9e9
    Collections
    • Journal Articles (CI) [118]

    MUT Library copyright © 2017-2024  MUT Library Website
    Contact Us | Send Feedback
     

     

    Browse

    All of Research ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    MUT Library copyright © 2017-2024  MUT Library Website
    Contact Us | Send Feedback