A review of insider threat detection model for government agencies

In the cybersecurity world, combating and preventing insider threat is one of the biggest issues that has been tried to sort out by many organizations. Government agencies became a special target of attack since it involves a high value of dataset and information. According to weakest link theory, i...

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Bibliographic Details
Main Authors: Samy, Ganthan Narayana, Hassan, Noor Hafizah, Ishak, Ruzana, Mohd. Azmi, Nurulhuda Firdaus, Bahari, Rokiah, Maarop, Nurazean, Radhakrishnan, Mugilraj
Format: Article
Published: Open International Journal of Informatics (OIJI) 2018
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Online Access:http://eprints.utm.my/id/eprint/82176/
http://apps.razak.utm.my
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Summary:In the cybersecurity world, combating and preventing insider threat is one of the biggest issues that has been tried to sort out by many organizations. Government agencies became a special target of attack since it involves a high value of dataset and information. According to weakest link theory, it is said human are the weakest link in an organization compared to other arising security issues. The existing traditional security appliances and basic safeguards that are meant to prevent the insider threat are no longer relevant to the situation. The government agencies need to address the insider threat beyond the technological dimension in order to give a holistic approach to insider threat. There is a need for government agencies to address multiple dimensions that influence the insider to perform and rationalize malicious attack. Therefore, the government agencies should develop an insider threat detection model that addresses interrelated domains to detect and mitigate the insider threat. This paper aimed at reviewing existing available insider threat models and solutions and to identify the most relevant solutions to government agencies. The paper also suggests the threat dimensions that needed to be considered in order develop an insider detection model in later stages to mitigate the insider threat events.