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A machine learning approach to detect insider threats in emails caused by human behaviour
Dissertation (MSc (Computer Science))--University of Pretoria, 2020.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613641531981824 |
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| access_status_str | Open Access |
| author2 | Eloff, Jan H.P. |
| author_browse | Eloff, Jan H.P. |
| author_facet | Eloff, Jan H.P. |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Computer Science))--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78129 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:22.809Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/78129 A machine learning approach to detect insider threats in emails caused by human behaviour Eloff, Jan H.P. tonia.michael94@gmail.com Michael, Antonia Big Data Insider Threat Detection Insider Threats Emails Cybersecurity Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Dissertation (MSc (Computer Science))--University of Pretoria, 2020. In recent years, there has been a significant increase in insider threats within organisations and these have caused massive losses and damages. Due to the fact that email communications are a crucial part of the modern-day working environment, many insider threats exist within organisations’ email infrastructure. It is a well-known fact that employees not only dispatch ‘business-as-usual’ emails, but also emails that are completely unrelated to company business, perhaps even involving malicious activity and unethical behaviour. Such insider threat activities are mostly caused by employees who have legitimate access to their organisation’s resources, servers, and non-public data. However, these same employees abuse their privileges for personal gain or even to inflict malicious damage on the employer. The problem is that the high volume and velocity of email communication make it virtually impossible to minimise the risk of insider threat activities, by using techniques such as filtering and rule-based systems. The research presented in this dissertation suggests strategies to minimise the risk of insider threat via email systems by employing a machine-learning-based approach. This is done by studying and creating categories of malicious behaviours posed by insiders, and mapping these to phrases that would appear in email communications. Furthermore, a large email dataset is classified according to behavioural characteristics of employees. Machine learning algorithms are employed to identify commonly occurring insider threats and to group the occurrences according to insider threat classifications. bs2026 Computer Science MSc (Computer Science) Unrestricted SDG-09: Industry, innovation and infrastructure SDG-16: Peace, justice and strong institutions 2021-01-26T09:12:32Z 2021-01-26T09:12:32Z 2021 2020 Dissertation * A2021 http://hdl.handle.net/2263/78129 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Big Data Insider Threat Detection Insider Threats Emails Cybersecurity Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 A machine learning approach to detect insider threats in emails caused by human behaviour |
| title | A machine learning approach to detect insider threats in emails caused by human behaviour |
| title_full | A machine learning approach to detect insider threats in emails caused by human behaviour |
| title_fullStr | A machine learning approach to detect insider threats in emails caused by human behaviour |
| title_full_unstemmed | A machine learning approach to detect insider threats in emails caused by human behaviour |
| title_short | A machine learning approach to detect insider threats in emails caused by human behaviour |
| title_sort | machine learning approach to detect insider threats in emails caused by human behaviour |
| topic | Big Data Insider Threat Detection Insider Threats Emails Cybersecurity Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 |
| url | http://hdl.handle.net/2263/78129 |