Users’ Scenario-Base for Analysing Insider Threat Detection Based on User’s Downloads Activity Logs

Author:

Padiet Peter,Islam Rafiqul,Khan M. Arif

Publisher

Springer Nature Switzerland

Reference30 articles.

1. Ispahany, J., Islam, R.: Detecting malicious COVID-19 URLs using machine learning techniques. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp. 718–723 (2021). https://doi.org/10.1109/PerComWorkshops51409.2021.9431064

2. Abrams, J.: Free covid-19 threat list - domain risk assessments for coronavirus threats (2020). https://www.domaintools.com/resources/blog/free-covid-19-threat-list-domain-risk-assessments-for-coronavirus-threats

3. Australia, N.: Australian broadband data demand: data demand on the nbn continues to reflect high network usage (2020). https://www.nbnco.com.au/corporate-information/media-centre/media-statements/data-demand-continues-to-reflect

4. Lavelle, J.: Gartner CFO survey reveals 74% intend to shift some employees to remote work permanently. Gartner 3 (2020)

5. Reeves, C.: Watch out for covid-19 phishing and malware, March 2020. https://exchange.telstra.com.au/watch-out-for-coronavirus-covid-19-phishing-and-malware/

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