Improving Cyber Situational Awareness via Data mining and Predictive Analytic Techniques

POURMOURI, Sina (2019). Improving Cyber Situational Awareness via Data mining and Predictive Analytic Techniques. Doctoral, Sheffield Hallam University.

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Link to published version:: https://doi.org/10.7190/shu-thesis-00202
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    Abstract

    As cyber-attacks have become more common in everyday life, there is a need for maintaining and improving cyber security standards in any business or industry. Cyber Situational Awareness (CSA) is a broad strategy which can be adopted by any business or government to tackle cyber-attacks and incidents. CSA is based on current and past incidents, elements and actors in any system. Managers and decision makers need to monitor their systems constantly to understand ongoing events and changes which it can lead to predict future incidents. Prediction of future cyber incidents then can guide cyber managers to be prepared against future cyber threats and breaches. This research aims to improve cyber situational awareness by developing a framework based on data mining techniques specifically classification methods known as predictive approaches and Open Source Intelligence (OSINT). OSINT is another important element in this research because not only it is accessible publicly but also it is cost effective and research friendly. This research highlights the importance of understanding past and current CSA, which it can lead to more preparation against future cyber threats, and cyber security experts can use the developed framework with other different methods and provide a comprehensive strategy to improve cyber security and safety.

    Item Type: Thesis (Doctoral)
    Additional Information: Director of studies: Babak Akhgar "No PQ harvesting"
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
    Identification Number: https://doi.org/10.7190/shu-thesis-00202
    Depositing User: Colin Knott
    Date Deposited: 31 Jul 2019 10:07
    Last Modified: 08 Mar 2020 01:18
    URI: http://shura.shu.ac.uk/id/eprint/24949

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